The document provides an overview of techniques for secure speech communication, including speech coding, speaker identification, and encryption/decryption. It discusses various speech coding techniques like waveform coding, parametric coding, and hybrid coding that can compress speech signals while maintaining quality. It also describes speaker identification methods using hashing to authenticate users. For encryption, it outlines symmetric techniques like AES that use a shared key, and asymmetric techniques like RSA that use public/private key pairs. The goal is to integrate these methods to provide a high level of security for speech communication by removing redundancy, authenticating speakers, and strongly encrypting signals.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
This document summarizes a research paper on speaker identification using artificial neural networks. The paper presents a speaker identification system that uses digital signal processing and ANN techniques. Speech features are extracted from utterances using FFT and windowing. These features are used to train a multi-layer perceptron network to classify speakers. The system was tested on Bangla speech and achieved accurate identification of speakers from their utterances.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using back propagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Voice Recognition System using Template MatchingIJORCS
It is easy for human to recognize familiar voice but using computer programs to identify a voice when compared with others is a herculean task. This is due to the problem that is encountered when developing the algorithm to recognize human voice. It is impossible to say a word the same way in two different occasions. Human speech analysis by computer gives different interpretation based on varying speed of speech delivery. This research paper gives detail description of the process behind implementation of an effective voice recognition algorithm. The algorithm utilize discrete Fourier transform to compare the frequency spectra of two voice samples because it remained unchanged as speech is slightly varied. Chebyshev inequality is then used to determine whether the two voices came from the same person. The algorithm is implemented and tested using MATLAB.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes research on speaker recognition in noisy environments. It begins with an introduction discussing the goals of speaker identification and verification and their applications. It then provides details on the basic components of a speaker recognition system, including feature extraction and classification. The document focuses on methods for modeling noise, including generating multiple noisy training conditions and focusing matching on unaffected features. Experimental results are shown through snapshots of a prototype system interface that allows adding and recognizing speakers based on voice samples. The system is able to identify speakers in the presence of noise by comparing features to stored codebooks generated during training.
This document summarizes and compares entropy and dictionary-based techniques for lossless data compression. It discusses how entropy coding like Huffman coding assigns shorter codes to more frequent symbols, making it well-suited for JPEG images. Dictionary techniques like LZW replace strings with codes, performing better on files with repetitive data like TIFFs. While entropy coding has simpler encoding, dictionary methods have better compression ratios and decoding capability. The document also presents the proposed Huffman coding of a bitmap image to assign variable-length codes based on color probabilities.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
This document summarizes a research paper on speaker identification using artificial neural networks. The paper presents a speaker identification system that uses digital signal processing and ANN techniques. Speech features are extracted from utterances using FFT and windowing. These features are used to train a multi-layer perceptron network to classify speakers. The system was tested on Bangla speech and achieved accurate identification of speakers from their utterances.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using back propagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Voice Recognition System using Template MatchingIJORCS
It is easy for human to recognize familiar voice but using computer programs to identify a voice when compared with others is a herculean task. This is due to the problem that is encountered when developing the algorithm to recognize human voice. It is impossible to say a word the same way in two different occasions. Human speech analysis by computer gives different interpretation based on varying speed of speech delivery. This research paper gives detail description of the process behind implementation of an effective voice recognition algorithm. The algorithm utilize discrete Fourier transform to compare the frequency spectra of two voice samples because it remained unchanged as speech is slightly varied. Chebyshev inequality is then used to determine whether the two voices came from the same person. The algorithm is implemented and tested using MATLAB.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes research on speaker recognition in noisy environments. It begins with an introduction discussing the goals of speaker identification and verification and their applications. It then provides details on the basic components of a speaker recognition system, including feature extraction and classification. The document focuses on methods for modeling noise, including generating multiple noisy training conditions and focusing matching on unaffected features. Experimental results are shown through snapshots of a prototype system interface that allows adding and recognizing speakers based on voice samples. The system is able to identify speakers in the presence of noise by comparing features to stored codebooks generated during training.
This document summarizes and compares entropy and dictionary-based techniques for lossless data compression. It discusses how entropy coding like Huffman coding assigns shorter codes to more frequent symbols, making it well-suited for JPEG images. Dictionary techniques like LZW replace strings with codes, performing better on files with repetitive data like TIFFs. While entropy coding has simpler encoding, dictionary methods have better compression ratios and decoding capability. The document also presents the proposed Huffman coding of a bitmap image to assign variable-length codes based on color probabilities.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a fast algorithm for noisy speaker recognition using artificial neural networks. It summarizes the following:
- The algorithm first records voice patterns from speakers via a noisy channel and applies noise removal techniques.
- It then extracts features using Mel Frequency Cepstral Coefficients (MFCC) and reduces features using Principal Component Analysis.
- The reduced feature vectors are classified using an artificial neural network classifier.
- Experimental results showed the proposed algorithm achieved about 99% accuracy on average, which is higher than other methods, and was also faster.
IRJET- Speech to Speech Translation SystemIRJET Journal
1. The document describes a speech-to-speech translation system that aims to facilitate conversations between people speaking different languages.
2. It discusses the architecture of the proposed system, which includes modules for speech input, speech recognition, translation, grammar correction, text-to-speech synthesis, and speech output.
3. The document also reviews related work on speech recognition, translation, and text-to-speech systems. It outlines the implementation status of the different modules in the proposed system and possibilities for future improvement, such as supporting additional languages.
ADVANCED LSB TECHNIQUE FOR AUDIO STENOGRAPHYcsandit
This work contributes to the multimedia security fields by given that more protected
steganography technique which ensures message confidentiality and integrity. An Advanced
Least Significant Bit (ALSB) technique is presented in order to meet audio steganography
requirements, which are imperceptibility, capacity, and robustness. An extensive evaluation
study was conducted measuring the performance of proposed NLSB algorithm. A set of factors
were measured and used during evaluation, this includes; Peak Signal to Noise Ratio (PSNR)
and Bit Error Rate. MP3 Audio files from five different audio generators were used during
evaluation. Results indicated that ALSB outperforms standard Least Significant Bit (SLSB)
technique. Moreover, ALSB can be embedding an utmost of 750 kb into MP3 file size less than 2
MB with 30db average achieving enhanced capacity capability.
This document discusses feature extraction techniques for isolated word speech recognition. It begins with an introduction to digital speech processing and speech recognition models. The main part of the document compares two common feature extraction techniques: Mel Frequency Cepstral Coefficients (MFCC) and Relative Spectral (RASTA) filtering. MFCC allows signals to extract feature vectors and provides high performance but lacks robustness. RASTA filtering reduces the impact of noise in signals and provides high robustness by band-passing feature coefficients in both log spectral and spectral domains. The document provides details on the process of MFCC feature extraction, which involves steps like framing, windowing, fast Fourier transform, mel filtering, discrete cosine transform, and calculating
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH ...csandit
This document describes a study that developed a speech recognition system for recognizing spoken Malayalam digits. It used two wavelet-based feature extraction techniques - Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD) - and evaluated their performance using a Naive Bayes classifier. DWT achieved 83.5% accuracy and WPD achieved 80.7% accuracy. To improve recognition accuracy, the study introduced a new technique called Discrete Wavelet Packet Decomposition (DWPD) that utilizes features from both DWT and WPD. DWPD achieved the highest accuracy of 86.2% along with the Naive Bayes classifier.
MAF ICIMS™ Monitoring, Analytics & Reporting for Microsoft Teams and UC - glo...MAF InfoCom
MAF ICIMS™ is a reporting and analytics solution for Unified Communication and Collaboration (UC&C) platforms and other data sources such as Session Border Controllers (SBC’s), Gateways, Trading Platforms, Turrents & Dealer Boards. It allows you to gain valuable business and technical insights through its reports, daily dashboards and historical trend monitors. Its flexible, user defined nature means you tell the software what you want to see instead of the software dictating to you what you will see.
This document reviews techniques used in spoken-word recognition systems. It discusses popular feature extraction techniques like MFCC, LPC, DWT, WPD that are used to represent speech signals in a compact form before classification. Classification techniques discussed are ANN, HMM, DTW, and VQ. The document provides a brief overview of each technique and their advantages. It also presents the generalized workflow of a spoken-word recognition system including stages of speech acquisition, pre-emphasis, feature extraction, modeling, classification, and output of recognized text.
Robust Speech Recognition Technique using Mat labIRJET Journal
The document proposes a new robust speech recognition technique using MATLAB. It takes an audio signal as input, eliminates noise, and matches patterns to recognize the input. It uses Fourier transforms to extract data from the audio, performs noise elimination, converts the noise-eliminated data to patterns, and matches the patterns to those stored in a database. Patterns are represented as states in a weighted automaton. The technique achieves 90% accuracy in recognizing words and is well-suited for voice command systems.
Automatic speech recognition system using deep learningAnkan Dutta
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
Classification of Language Speech Recognition Systemijtsrd
This paper is aimed to implement Classification of Language Speech Recognition System by using feature extraction and classification. It is an Automatic language Speech Recognition system. This system is a software architecture which outputs digits from the input speech signals. The system is emphasized on Speaker Dependent Isolated Word Recognition System. To implement this system, a good quality microphone is required to record the speech signals. This system contains two main modules feature extraction and feature matching. Feature extraction is the process of extracting a small amount of data from the voice signal that can later be used to represent each speech signal. Feature matching involves the actual procedure to identify the unknown speech signal by comparing extracted features from the voice input of a set of known speech signals and the decision making process. In this system, the Mel frequency Cepstrum Coefficient MFCC is used for feature extraction and Vector Quantization VQ which uses the LBG algorithm is used for feature matching. Khin May Yee | Moh Moh Khaing | Thu Zar Aung "Classification of Language Speech Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26546.pdfPaper URL: https://www.ijtsrd.com/computer-science/speech-recognition/26546/classification-of-language-speech-recognition-system/khin-may-yee
Survey of universal authentication protocol for mobile communicationAhmad Sharifi
This document provides a survey of universal authentication protocols. It begins with an introduction to authentication protocols and some examples of protocols that were later found to have flaws. It then discusses the cryptographic prerequisites for authentication protocols, including symmetric key cryptography, classical cryptography, and modern cryptography. The document describes several block cipher algorithms and modes of using block ciphers. The overall purpose is to provide an overview of authentication protocols and related cryptographic concepts.
LPC Models and Different Speech Enhancement Techniques- A Reviewijiert bestjournal
Author has already published one review paper on the quality enhancement of a speech signal by minimizing the noise. This is a second paper of same series. In last two decades the researchers have taken continuous efforts to reduce the noise signal from the speech signal. Th is paper comments on,various study carried out and analysis propos als of the researchers for en hancement of the quality of speech signal. Various models,coding,speech quality improvement methods,speaker dependent codebooks,autocorrelation subtraction,speech restoration,producing speech at low bit rates,compression and enhancement are the vari ous aspects of speech enhancement. We have presented the review of all above mentioned technologies in this paper and also willing to examine few of the techniques in order to analyze the factors affecting them in upcoming paper of the series.
Shared-hidden-layer Deep Neural Network for Under-resourced Language the ContentTELKOMNIKA JOURNAL
Training speech recognizer with under-resourced language data still proves difficult. Indonesian language is considered under-resourced because the lack of a standard speech corpus, text corpus, and dictionary. In this research, the efficacy of augmenting limited Indonesian speech training data with highly-resourced-language training data, such as English, to train Indonesian speech recognizer was analyzed. The training was performed in form of shared-hidden-layer deep-neural-network (SHL-DNN) training. An SHL-DNN has language-independent hidden layers and can be pre-trained and trained using multilingual training data without any difference with a monolingual deep neural network. The SHL-DNN using Indonesian and English speech training data proved effective for decreasing word error rate (WER) in decoding Indonesian dictated-speech by achieving 3.82% absolute decrease compared to a monolingual Indonesian hidden Markov model using Gaussian mixture model emission (GMM-HMM). The case was confirmed when the SHL-DNN was also employed to decode Indonesian spontaneous-speech by achieving 4.19% absolute WER decrease.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Speech compression analysis using matlabeSAT Journals
This document discusses speech compression analysis using MATLAB. It begins with an introduction to speech compression, noting its importance for efficient storage and transmission of audio data. It then discusses various speech compression techniques, including lossy and lossless compression as well as standards like MPEG. It focuses on using the discrete cosine transform and MATLAB commands to analyze speech signals, including reading wav files, applying windowing functions and the DCT, and playing/viewing the output. The document concludes by discussing current applications of speech compression technologies like MPEG.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Berrymans B&B Horse Riding Farm offers horse riding lessons, treks, and trails for visitors of all ages and abilities in County Antrim, Northern Ireland, with a passionate team dedicated to providing memorable horse riding experiences through their riding school, hacking trails, and cross-country riding on their 280 acre farm located near scenic areas.
Este documento presenta un resumen de un libro titulado "El Asesinato del Profe de Matemáticas". Narra la historia de tres estudiantes que tienen problemas con las matemáticas y su profesor Felipe Romero, quien les da la oportunidad de aprobar la asignatura mediante un examen con pistas sobre un supuesto asesinato, despertando el interés de los estudiantes por las matemáticas a través de acertijos y juegos.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a fast algorithm for noisy speaker recognition using artificial neural networks. It summarizes the following:
- The algorithm first records voice patterns from speakers via a noisy channel and applies noise removal techniques.
- It then extracts features using Mel Frequency Cepstral Coefficients (MFCC) and reduces features using Principal Component Analysis.
- The reduced feature vectors are classified using an artificial neural network classifier.
- Experimental results showed the proposed algorithm achieved about 99% accuracy on average, which is higher than other methods, and was also faster.
IRJET- Speech to Speech Translation SystemIRJET Journal
1. The document describes a speech-to-speech translation system that aims to facilitate conversations between people speaking different languages.
2. It discusses the architecture of the proposed system, which includes modules for speech input, speech recognition, translation, grammar correction, text-to-speech synthesis, and speech output.
3. The document also reviews related work on speech recognition, translation, and text-to-speech systems. It outlines the implementation status of the different modules in the proposed system and possibilities for future improvement, such as supporting additional languages.
ADVANCED LSB TECHNIQUE FOR AUDIO STENOGRAPHYcsandit
This work contributes to the multimedia security fields by given that more protected
steganography technique which ensures message confidentiality and integrity. An Advanced
Least Significant Bit (ALSB) technique is presented in order to meet audio steganography
requirements, which are imperceptibility, capacity, and robustness. An extensive evaluation
study was conducted measuring the performance of proposed NLSB algorithm. A set of factors
were measured and used during evaluation, this includes; Peak Signal to Noise Ratio (PSNR)
and Bit Error Rate. MP3 Audio files from five different audio generators were used during
evaluation. Results indicated that ALSB outperforms standard Least Significant Bit (SLSB)
technique. Moreover, ALSB can be embedding an utmost of 750 kb into MP3 file size less than 2
MB with 30db average achieving enhanced capacity capability.
This document discusses feature extraction techniques for isolated word speech recognition. It begins with an introduction to digital speech processing and speech recognition models. The main part of the document compares two common feature extraction techniques: Mel Frequency Cepstral Coefficients (MFCC) and Relative Spectral (RASTA) filtering. MFCC allows signals to extract feature vectors and provides high performance but lacks robustness. RASTA filtering reduces the impact of noise in signals and provides high robustness by band-passing feature coefficients in both log spectral and spectral domains. The document provides details on the process of MFCC feature extraction, which involves steps like framing, windowing, fast Fourier transform, mel filtering, discrete cosine transform, and calculating
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH ...csandit
This document describes a study that developed a speech recognition system for recognizing spoken Malayalam digits. It used two wavelet-based feature extraction techniques - Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD) - and evaluated their performance using a Naive Bayes classifier. DWT achieved 83.5% accuracy and WPD achieved 80.7% accuracy. To improve recognition accuracy, the study introduced a new technique called Discrete Wavelet Packet Decomposition (DWPD) that utilizes features from both DWT and WPD. DWPD achieved the highest accuracy of 86.2% along with the Naive Bayes classifier.
MAF ICIMS™ Monitoring, Analytics & Reporting for Microsoft Teams and UC - glo...MAF InfoCom
MAF ICIMS™ is a reporting and analytics solution for Unified Communication and Collaboration (UC&C) platforms and other data sources such as Session Border Controllers (SBC’s), Gateways, Trading Platforms, Turrents & Dealer Boards. It allows you to gain valuable business and technical insights through its reports, daily dashboards and historical trend monitors. Its flexible, user defined nature means you tell the software what you want to see instead of the software dictating to you what you will see.
This document reviews techniques used in spoken-word recognition systems. It discusses popular feature extraction techniques like MFCC, LPC, DWT, WPD that are used to represent speech signals in a compact form before classification. Classification techniques discussed are ANN, HMM, DTW, and VQ. The document provides a brief overview of each technique and their advantages. It also presents the generalized workflow of a spoken-word recognition system including stages of speech acquisition, pre-emphasis, feature extraction, modeling, classification, and output of recognized text.
Robust Speech Recognition Technique using Mat labIRJET Journal
The document proposes a new robust speech recognition technique using MATLAB. It takes an audio signal as input, eliminates noise, and matches patterns to recognize the input. It uses Fourier transforms to extract data from the audio, performs noise elimination, converts the noise-eliminated data to patterns, and matches the patterns to those stored in a database. Patterns are represented as states in a weighted automaton. The technique achieves 90% accuracy in recognizing words and is well-suited for voice command systems.
Automatic speech recognition system using deep learningAnkan Dutta
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
Classification of Language Speech Recognition Systemijtsrd
This paper is aimed to implement Classification of Language Speech Recognition System by using feature extraction and classification. It is an Automatic language Speech Recognition system. This system is a software architecture which outputs digits from the input speech signals. The system is emphasized on Speaker Dependent Isolated Word Recognition System. To implement this system, a good quality microphone is required to record the speech signals. This system contains two main modules feature extraction and feature matching. Feature extraction is the process of extracting a small amount of data from the voice signal that can later be used to represent each speech signal. Feature matching involves the actual procedure to identify the unknown speech signal by comparing extracted features from the voice input of a set of known speech signals and the decision making process. In this system, the Mel frequency Cepstrum Coefficient MFCC is used for feature extraction and Vector Quantization VQ which uses the LBG algorithm is used for feature matching. Khin May Yee | Moh Moh Khaing | Thu Zar Aung "Classification of Language Speech Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26546.pdfPaper URL: https://www.ijtsrd.com/computer-science/speech-recognition/26546/classification-of-language-speech-recognition-system/khin-may-yee
Survey of universal authentication protocol for mobile communicationAhmad Sharifi
This document provides a survey of universal authentication protocols. It begins with an introduction to authentication protocols and some examples of protocols that were later found to have flaws. It then discusses the cryptographic prerequisites for authentication protocols, including symmetric key cryptography, classical cryptography, and modern cryptography. The document describes several block cipher algorithms and modes of using block ciphers. The overall purpose is to provide an overview of authentication protocols and related cryptographic concepts.
LPC Models and Different Speech Enhancement Techniques- A Reviewijiert bestjournal
Author has already published one review paper on the quality enhancement of a speech signal by minimizing the noise. This is a second paper of same series. In last two decades the researchers have taken continuous efforts to reduce the noise signal from the speech signal. Th is paper comments on,various study carried out and analysis propos als of the researchers for en hancement of the quality of speech signal. Various models,coding,speech quality improvement methods,speaker dependent codebooks,autocorrelation subtraction,speech restoration,producing speech at low bit rates,compression and enhancement are the vari ous aspects of speech enhancement. We have presented the review of all above mentioned technologies in this paper and also willing to examine few of the techniques in order to analyze the factors affecting them in upcoming paper of the series.
Shared-hidden-layer Deep Neural Network for Under-resourced Language the ContentTELKOMNIKA JOURNAL
Training speech recognizer with under-resourced language data still proves difficult. Indonesian language is considered under-resourced because the lack of a standard speech corpus, text corpus, and dictionary. In this research, the efficacy of augmenting limited Indonesian speech training data with highly-resourced-language training data, such as English, to train Indonesian speech recognizer was analyzed. The training was performed in form of shared-hidden-layer deep-neural-network (SHL-DNN) training. An SHL-DNN has language-independent hidden layers and can be pre-trained and trained using multilingual training data without any difference with a monolingual deep neural network. The SHL-DNN using Indonesian and English speech training data proved effective for decreasing word error rate (WER) in decoding Indonesian dictated-speech by achieving 3.82% absolute decrease compared to a monolingual Indonesian hidden Markov model using Gaussian mixture model emission (GMM-HMM). The case was confirmed when the SHL-DNN was also employed to decode Indonesian spontaneous-speech by achieving 4.19% absolute WER decrease.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Speech compression analysis using matlabeSAT Journals
This document discusses speech compression analysis using MATLAB. It begins with an introduction to speech compression, noting its importance for efficient storage and transmission of audio data. It then discusses various speech compression techniques, including lossy and lossless compression as well as standards like MPEG. It focuses on using the discrete cosine transform and MATLAB commands to analyze speech signals, including reading wav files, applying windowing functions and the DCT, and playing/viewing the output. The document concludes by discussing current applications of speech compression technologies like MPEG.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Berrymans B&B Horse Riding Farm offers horse riding lessons, treks, and trails for visitors of all ages and abilities in County Antrim, Northern Ireland, with a passionate team dedicated to providing memorable horse riding experiences through their riding school, hacking trails, and cross-country riding on their 280 acre farm located near scenic areas.
Este documento presenta un resumen de un libro titulado "El Asesinato del Profe de Matemáticas". Narra la historia de tres estudiantes que tienen problemas con las matemáticas y su profesor Felipe Romero, quien les da la oportunidad de aprobar la asignatura mediante un examen con pistas sobre un supuesto asesinato, despertando el interés de los estudiantes por las matemáticas a través de acertijos y juegos.
O documento discute cinco mitos sobre nutrição e exercício físico: 1) fazer exercício em jejum não é recomendado, pois pode reduzir o metabolismo e aumentar o risco de fraqueza; 2) é importante ingerir carboidratos de baixo índice glicêmico antes do treino para queimar mais gordura; 3) suplementos de carboidratos são benéficos mesmo para treinos curtos de alta intensidade; 4) não é necessário tomar suplementos de whey protein para ganhar massa muscular, proteína pode ser obt
Este documento proporciona información sobre el curso de Mercadotecnia I que se impartirá en la Pontificia Universidad Católica del Ecuador Sede Ambato. El curso se llevará a cabo durante el período académico de enero a mayo de 2014 y tendrá una carga de 4 créditos. El curso cubrirá temas relacionados con el análisis del ambiente de mercadeo, oportunidades de mercado, selección de mercados objetivo, y la mezcla de mercadotecnia. El docente será
This document outlines four collaborative learning techniques using Blackboard: 1) Reading response blogs for groups where students are assigned to groups and take turns blogging about assigned readings and responding to each other's posts, 2) Issue debates where students schedule in-class debates and use blogs to prepare and discuss, 3) Test debriefing discussion boards where students discuss test questions after a test to learn from each other, and 4) Expert forums where designated student experts create a wiki to post audio and written responses to questions from other students on a topic.
O documento resume o STB Student Surfing Festival de 2011, que reuniu 84 atletas de 35 escolas em Curl Curl Beach, na Austrália, para competições nas categorias International Students, University + Vocational, High School, Girls, Tag Team e Pasti XPression Session. A escola Embassy - Study Group foi a grande campeã da categoria Tag Team.
The document discusses the concept of the technological singularity, which is the hypothetical point in time when artificial intelligence will surpass human intelligence and continue to exponentially self-improve, resulting in unfathomable changes. Vernor Vinge introduced this idea in 1993, arguing that exponential growth in technology will reach a point where the future cannot be predicted. The document outlines key aspects of the singularity like accelerating progress, positive feedback loops, and the idea that humanity may be unable to comprehend advanced AI once it surpasses human intelligence.
My presentation on the "Geometric and Visual Computing Seminar" at the Universita della Svizzera italiana. The topic covered is generalized barycentric coordinates for convex polygons. At the beginning I do some short introduction into what is barycentric coordinates and then consider two types of generalization of these coordinates to convex polygons namely Wachspress and Mean Value Coordinates.
Date of presentation: April 2012
For preparing my slides I take pictures and some other information from the internet and I try to use only legal one. But if I did not notice something and you have Rights for any kind of this information and do not want to see it in the presentation please let me know and I will remove it from the slides as fast as possible or remove the slides themselves. Thanks for your collaboration.
El documento resume los principales conceptos de la propiedad intelectual relacionados con la agricultura. Explica que la propiedad intelectual incluye el derecho de autor, la propiedad industrial (patentes, marcas, diseños industriales y secretos comerciales) y las variedades vegetales. También describe brevemente el CGIAR y su trabajo para generar bienes públicos internacionales en investigación agrícola y el Tratado Internacional sobre los Recursos Fitogenéticos.
Este documento lista jogos de futebol e futsal agendados entre 8 e 14 de outubro de 2010 pela Associação de Futebol de Leiria. Inclui nomeações de árbitros para partidas envolvendo equipes de diversas categorias como seniores, juniores, juvenis e inicados.
Este documento presenta los principales conceptos del enfoque piagetiano sobre el aprendizaje y la construcción del conocimiento. Explica que para Piaget, el conocimiento se construye a través de procesos de asimilación y acomodación que ocurren en diferentes etapas del desarrollo. También describe que la interacción social y las experiencias con objetos permiten alcanzar equilibrios cognitivos superiores.
Prefeitura pede apoio dos empresários para projetos sociais. Agentes do Banco do Povo visitarão ambulantes nas praias para apresentar linhas de crédito para investimentos. Município orienta ambulantes sobre linhas de crédito. ONG leva espetáculo de balé à praça pública.
Este documento resume un estudio que examinó los efectos del enfriamiento evaporativo en la regulación del agua y la producción de leche en ganado mestizo Holstein en un ambiente tropical. El estudio dividió 36 vacas lecheras en dos grupos, uno en un establo con enfriamiento evaporativo y otro en un establo abierto. Los resultados mostraron que las vacas enfriadas produjeron un 42%, 36% y 79% más leche en las primeras, medias y últimas etapas de la lactancia respectivamente. El enfriamiento también redujo la temperatura corporal
Este resumen presenta la información clave de un documento sobre un libro llamado "Matemática... ¿Estás ahí?". El documento incluye un índice con secciones como introducción, contenido, conclusión y actividad. También presenta extractos de lecturas sobre temas matemáticos como juegos de ajedrez, monedas en un círculo y partidos de tenis.
Working with Web 2.0 APIs (or, maybe just defining)Bridget S
This document discusses APIs and how they allow different applications to share data. It defines APIs and Web 2.0, noting that APIs connect applications and Web 2.0 enables collaboration and sharing online. Developers use APIs to create mashups, which combine data from multiple online sources into a single tool. Popular APIs include Google Maps, Facebook, Flickr, YouTube, and Twitter. The document provides examples of mashups and additional readings on APIs and Web 2.0.
O documento resume o relatório do Índice de Preços ao Consumidor (IPC) na Região Metropolitana de Belém (RMB) para o mês de junho de 2013. A taxa de inflação foi de 0,50%, abaixo dos 0,95% do mês anterior, principalmente devido à queda nos preços dos alimentos. Alguns itens como feijão e serviços continuaram pressionando os preços para cima. O acumulado em 12 meses foi de 12,36% e em 2013 foi de 5,81%.
IRJET- Voice Command Execution with Speech Recognition and SynthesizerIRJET Journal
The document describes a voice command execution system using speech recognition and text-to-speech synthesis. The proposed system allows users to complete tasks using only voice commands, reducing time delays compared to traditional systems requiring mouse/keyboard input. It recognizes three types of voice commands - social commands for question answering, web commands to access URLs, and shell commands involving file/application directories. A speech synthesizer converts text to speech to provide output to the user. The system aims to enable hands-free computing for disabled users by executing commands with only voice.
Implementation of Huffman Decoder on FpgaIJERA Editor
Lossless data compression algorithm is most widely used algorithm in data transmission, reception and storage
systems in order to increase data rate, speed and save lots of space on storage devices. Now-a-days, different
algorithms are implemented in hardware to achieve benefits of hardware realizations. Hardware implementation
of algorithms, digital signal processing algorithms and filter realization is done on programmable devices i.e.
FPGA. In lossless data compression algorithms, Huffman algorithm is most widely used because of its variable
length coding features and many other benefits. Huffman algorithms are used in many applications in software
form, e.g. Zip and Unzip, communication, etc. In this paper, Huffman algorithm is implemented on Xilinx
Spartan 3E board. This FPGA is programmed by Xilinx tool, Xilinx ISE 8.2i. The program is written in VHDL
and text data is decoded by a Huffman algorithm on Hardware board which was previously encoded by
Huffman algorithm. In order to visualize the output clearly in waveforms, the same code is simulated on
ModelSim v6.4. Huffman decoder is also implemented in the MATLAB for verification of operation. The
FPGA is a configurable device which is more efficient in all aspects. Text application, image processing, video
streaming and in many other applications Huffman algorithms are implemented.
Implementation of Huffman Decoder on FpgaIJERA Editor
Lossless data compression algorithm is most widely used algorithm in data transmission, reception and storage systems in order to increase data rate, speed and save lots of space on storage devices. Now-a-days, different algorithms are implemented in hardware to achieve benefits of hardware realizations. Hardware implementation of algorithms, digital signal processing algorithms and filter realization is done on programmable devices i.e. FPGA. In lossless data compression algorithms, Huffman algorithm is most widely used because of its variable length coding features and many other benefits. Huffman algorithms are used in many applications in software form, e.g. Zip and Unzip, communication, etc. In this paper, Huffman algorithm is implemented on Xilinx Spartan 3E board. This FPGA is programmed by Xilinx tool, Xilinx ISE 8.2i. The program is written in VHDL and text data is decoded by a Huffman algorithm on Hardware board which was previously encoded by Huffman algorithm. In order to visualize the output clearly in waveforms, the same code is simulated on ModelSim v6.4. Huffman decoder is also implemented in the MATLAB for verification of operation. The FPGA is a configurable device which is more efficient in all aspects. Text application, image processing, video streaming and in many other applications Huffman algorithms are implemented.
Speech compression using loosy predictive coding (lpc)Harshal Ladhe
The paper presents a system for encoding speech at a low bit rate using Loosy Predictive Coding (LPC). LPC uses a 10th order Levinson-Durbin recursion algorithm to accurately estimate speech parameters in a computationally efficient manner. Speech from both male and female speakers was coded. The system was able to code speech at relatively low bit rates while maintaining good quality. LPC models human speech production and can achieve a bit rate of 2400 bits/second, making it suitable for secure telephone systems where meaning is prioritized over quality. LPC breaks sound into segments, sending information on voicing, pitch, and vocal tract to the decoder to reproduce the original speech.
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The Evaluation of a Code-Switched Sepedi-English Automatic Speech Recognition...IJCI JOURNAL
Speech technology is a field that encompasses various techniques and tools used to enable machines to interact with speech, such as automatic speech recognition (ASR), spoken dialog systems, and others, allowing a device to capture spoken words through a microphone from a human speaker. End-to-end approaches such as Connectionist Temporal Classification (CTC) and attention-based methods are the most used for the development of ASR systems. However, these techniques were commonly used for research and development for many high-resourced languages with large amounts of speech data for training and evaluation, leaving low-resource languages relatively underdeveloped. While the CTC method has been successfully used for other languages, its effectiveness for the Sepedi language remains uncertain. In this study, we present the evaluation of the Sepedi-English code-switched automatic speech recognition system. This end-to-end system was developed using the Sepedi Prompted Code Switching corpus and the CTC approach. The performance of the system was evaluated using both the NCHLT Sepedi test corpus and the Sepedi Prompted Code Switching corpus. The model produced the lowest WER of 41.9%, however, the model faced challenges in recognizing the Sepedi only text.
A comparison of different support vector machine kernels for artificial speec...TELKOMNIKA JOURNAL
As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
The document discusses different techniques for compressing multimedia data such as text, images, audio and video. It describes how compression works by removing redundancy in digital data and exploiting properties of human perception. It then explains different compression methods including lossless compression, lossy compression, entropy encoding, and specific algorithms like Huffman encoding and arithmetic coding. The goal of compression is to reduce the size of files to reduce storage and bandwidth requirements for transmission.
HIDING A MESSAGE IN MP3 USING LSB WITH 1, 2, 3 AND 4 BITSIJCNCJournal
This document summarizes a research paper that proposes a new steganography method for hiding text messages in MP3 audio files. The method randomly selects positions in the MP3 file to embed bits of the text message using the least significant bit (LSB) technique. The text message is embedded starting and ending with a unique signature or key. The methodology focuses on embedding one, two, three or four bits from the secret message into the MP3 file using LSB. The performance is evaluated based on robustness, imperceptibility and hiding capacity. Experimental results show the new method provides increased security compared to other LSB steganography methods.
MULTILINGUAL SPEECH TO TEXT CONVERSION USING HUGGING FACE FOR DEAF PEOPLEIRJET Journal
The document describes a system for multilingual speech-to-text conversion using Hugging Face that aims to assist deaf individuals. The system uses a Transformer-based encoder-decoder model trained on audio datasets. Feature extraction and tokenization are performed on the audio inputs. The model is fine-tuned using Hugging Face and evaluated based on word error rate. A web application allows users to record speech via microphone and see the transcription output. The implemented model achieved a 32.42% word error rate on a Hindi language dataset. The goal is to enable seamless communication for those with hearing impairments across multiple languages.
A survey on Enhancements in Speech RecognitionIRJET Journal
This document discusses enhancements in speech recognition and provides an overview of the history and basic model of speech recognition. It summarizes key enhancements researchers have made to improve speech recognition, especially in noisy environments. The basic model of speech recognition involves speech input, preprocessing using techniques like MFCCs, classification models like RNNs and HMMs, and output of a transcript. Researchers are working to develop robust speech recognition that can understand speech in any environment.
Comparisons of QoS in VoIP over WIMAX by Varying the Voice codes and Buffer sizeEditor IJCATR
Voice over Internet Protocol (VoIP) is developed for voice communications system based on voice packets transmitted over
IP network with real-time communications of voice across networks using the Internet protocols. Quality of Service (QoS) mechanism
is applied to guarantee successful voice packets transmitted over IP network with reduced delay or drop according to assigned priority
of voice packets. In this paper, the goal of simulation models is present to investigate the performance of VoIP codecs and buffer size
for improving quality of service (QoS) with the simulation results by using OPNET modeler version 14.5. The performance of the
proposed algorithm is analyzed and compared the quality of service for VoIP. The final simulated result shows that the VoIP service
performance best under G.729 voice encoder scheme and buffer size 256 Kb over WiMAX network.
An efficient transcoding algorithm for G.723.1 and G.729A ...Videoguy
This document proposes an efficient transcoding algorithm to translate between the G.723.1 and G.729A speech coding standards. The algorithm has four main steps: 1) converting line spectral pair parameters between the two codecs, 2) converting pitch intervals using a smoothing technique, 3) performing a fast adaptive codebook search, and 4) performing a fast fixed codebook search. Objective and subjective tests show the algorithm achieves comparable speech quality to tandem encoding/decoding with 26-38% lower complexity and shorter delay.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Encrypting an audio file based on integer wavelet transform and hand geometryTELKOMNIKA JOURNAL
A new algorithm suggested for audio file encryption and decryption utilizing integer wavelet transform to take advantage of the property for adaptive context-based lossless audio coding. In addition, biometrics are used to give a significant level of classification and unwavering quality because the procedure has numerous qualities and points of interest. The offered algorithm utilized many properties of hand geometry estimations as keys to encode and decode the audio file. Many tests were carried out on a set of audio files and quality metrics such as mean square error and correlations were calculated which in turn confirmed the efficiency and quality of the work.
1. The document discusses various applications of deep learning algorithms for speaker identification and recognition, including convolutional deep belief networks (CDBN) and deep neural networks (DNN).
2. CDBN was shown to outperform traditional MFCC and raw features for audio classification tasks including speech and music recognition.
3. DNN approaches have demonstrated lower error rates than GMM-HMM models for speech recognition across multiple languages.
4. SIDEKIT is an open source Python toolkit that can implement state-of-the-art methods for speaker identification, including GMM-HMM, and has potential to incorporate DNN approaches.
This document discusses error-correcting codes that can be used to protect encoder and decoder circuitry in memory from soft errors. It introduces Euclidean Geometry Low-Density Parity-Check (EG-LDPC) codes that have fault-secure detector capabilities and can tolerate high bit or nanowire defect rates. Using some smaller EG-LDPC codes, the entire memory system failure-in-time rate can be kept at or below one for memory blocks of 10Mb or larger, with a memory density of 1011 bit/cm and 10nm nanowire pitch. Larger EG-LDPC codes can achieve even higher reliability and lower area overhead.
This document discusses various methods of data representation and compression. It begins by defining data representation as how information is conceived, manipulated, and stored, which can vary between environments. It then discusses several common data representation formats including ASCII, EBCDIC, and Shift-JIS character encodings as well as network data formats like ASN.1 and XDR. The document also covers popular image, audio, and video file compression standards and algorithms like JPEG, PNG, MP3, and MPEG.
This document summarizes a research paper on developing a speech-to-text conversion system for visually impaired people using μ-law companding. The system uses MATLAB to analyze input speech signals, extract features, filter noise, and match signals to samples stored in a database to convert speech to text. A graphical user interface was created to input speech and display recognition results. The system achieved real-time speech recognition and conversion to text with high accuracy using μ-law companding techniques for signal processing and correlation comparisons to the stored samples.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
National Security Agency - NSA mobile device best practices
B034205010
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 3 Issue 4 ǁ April 2014 ǁ PP.05-10
www.ijesi.org 5 | Page
Secure Speech Communication - A Review for Encrypted Data
1,
B.Archana Kiran , 2,
Dr.N.Chandra Shekar Reddy , Bashwanth
1,
M Tech in Software engineering Institute Of Aeronautical Engineering,Dundigal, Hyderabad-43
2,
Proff & Head of the Dept, Computer science & Engineering Institute Of Aeronautical Engineering,
Dundigal, Hyderabad-43
3,
Professor, CSE Dept.Institute of Aeronautical Engineering, Dundigal, Hyderabad-43
ABSTRACT : Secure speech communication has been of great significance in civil, commercial and military
communication systems. As speech communication happens to widely used and even more vulnerable, the
importance of providing a high level of security becomes a foremost issue. The main purpose of this paper is to
increase the security, and to remove the redundancy for speech communication system under the global
perspective of secure communication. So it deals with the integrating of speech coding, with speaker
confirmation and strong encryption. This paper also gives an overview and techniques accessible in speech
coding, speaker recognition, Encryption and Decryption. The main objective of this paper is to summarize some
of the well identified methods used in various stages for secure speech communication system.
KEYWORDS:Acoustic Environment, Speaker Authentication, Speech coding, Speech encryption with
decryption, Speaker Identification
I. INTRODUCTION
The increasing demand of transmission applications in communication system has covered the method
for secure communication. this can be necessary to beat unauthorized modifications and unwanted revealing
whereas transmission speech and different knowledge, particularly in wireless channels. In secure spoken
language systems the redundancy of the language plays a crucial role. The additional redundant the language,
the simpler it's for A persona non grata to decipher the data with ease and convenience. That’s why several real-
world scientific discipline implementations use a compression program to cut back the scale of the signal before
encoding. [1]. Compression of signal to lower rates with smart speech quality not solely eliminates the
redundancy issue however conjointly provides a lower information measure signal, that solves multiple issues in
communication and transmission applications [1]. The attainable threat that may attack in passive or active
method includes eavesdropping, modification, replay, masquerading, penetration and repudiation [2]. Speech is
employed by human to convey data to 1 another and this paper concentrates on the desegregation of speech
writing, recognition and powerful encoding for providing secure communication.This paper is organized as
follows. Section 2 gives details about the Speaker Identification Section 3 presents the Speech Coding. Section 4
deals with the Speech Encryption and Decryption. Section 5 explains about the Literature Survey Section 6
gives about the Proposed Methodology. Finally, the Conclusion is summarized in Section 7 with future work.
II. SPEAKER IDENTIFICATION
During communication the enemy may attempts to act as the authorized user and also tries to gain
access in it, so to avoid it the sender have to prove that he is an authenticate speaker against masquerade. A
masquerade is a kind of attack where the enemy attempts to act as the authorized user and also tries to gain
access in the communication .There by using control one may pass the operational order favorable to him which
may leads to vulnerability. In such cases a security alert is needed which will save information. So using speech
recognition algorithm, the speaker is identified. All the authorized speakers’ names are indexed and by using the
hashing algorithm the speaker can be retrieved from the database. The identified speaker can be authenticated
and by combining secure hashing algorithm bits with encoded speech the message digest is recovered. The hash
functions are used to compress an n (arbitrarily) large number of bits into a small number of bits (e.g. 512)
secure information is transmitted towards the receiver through different channels. If there is any mismatch the
destination recipient will get alert. There are many ideal cryptographic hash functions available [3]. Some of the
hashing functions are Message Digest, Secure Hash Algorithm (SHA), and RIPEMD, GOST and HAVAL.
Although there is a long list of hash functions many of the functions are found to be vulnerable. The SHA-0 and
SHA-1were developed by National security Agency. Still there is a competition for the replacement for SHA-
2[4], and also to ensure the long term toughness of applications that uses hash function.
2. Secure Speech Communication - A Review For…
www.ijesi.org 6 | Page
III. SPEECH CODING
The need for elimination, reduction of the redundancy or irrelevant information from the analog signals
and gave birth to an area of speech coding. Commercial systems that rely on efficient speech coding include
cellular communication, voice over internet protocol (VOIP), videoconferencing, electronic toys, archiving, and
digital simultaneous voice and data (DSVD), as well as numerous PC- based games and multimedia
applications. [5].The properties of speech coders include low bit rate, high speech quality, low coding delay,
robustness in the presence of channel errors [6]. The speech coding can be Lossy or Lossyless. In lossy
compression the actual data can be recovered from the compressed file. In Lossless compression, the actual data
cannot be retrieved from the compressed file even it gives best possible quality for the given technique [7]. The
quality of speech drops drastically if the encoding bit rate is reduced beyond a limit. Different speech coding
schemes have resulted into various speech codecs and it can be broadly classified depending upon the bit rate at
which they operate [8]. There is another way of classifying the speech coding techniques which is based on the
concepts utilized and it can broadly be categorized into:
❖ Waveform Coding
❖ Parametric Coding
❖ Hybrid Coding
3.1 Waveform Coding
Waveform coding is used to analyze code and reconstruct original speech sample by sample. It includes
time domain coding and frequency domain coding. The method such as Pulse Code Modulation (PCM),
Differential PCM (DPCM) [9], Adaptive DPCM (ADPCM), Delta Modulation (DM), and Adaptive PCMID are
some of the popular time domain waveform coding techniques and Transform Coding (IC), Sub band Coding
(SBC) are a few spectral domain waveform coding techniques. The Pulse Code Modulation (PCM) [9] which is
used to digitize the signals through signal conversion. Differential Pulse Code Modulation (DPCM) can be
analog signal or a digital signal. It uses the baseline of PCM but it adds some functionality based on the
prediction of the samples of the signal. In DPCM, first an estimate of each sample is found based on prediction
from past few samples and then the difference of estimate from the original. The DPCM can provide PCM
quality of speech at 56kbps. The ADPCM Adaptive Differential Pulse Code Modulation (ADPCM) [10] which
is used to provide much lower data rates by using a functional model of the human speaking mechanism at the
receiver end
[11] . The frequency domain includes sub band coding and the transform coding. Advantages with SBC is
that quantization noise in each band gets isolated from others and also bit rate optimization can be achieved by
assigning more number of bits to speech signal in lower frequency bands(that is responsible for intelligibility)
than in higher frequency bands. Variants of sub band coding are capable of providing speech at 9.6-32 kbps with
speech quality comparable to that of ADPCM and ADM. In transform coding the signal is transformed to its
representation in another domain in which it can be compressed well than in its original form.
3.2 Parametric Coding Methods
The parametric coding methods are capable of providing good quality of speech. Linear Predictive
Coding (LPC), Residual Excited Linear predictive Coding (RELP) and Mixed Excitation Linear Predictive
Coding (MELP) are the popular example under this class. The LPC method can produce intelligible speech at
2.4 kbps and it is one of the earliest speech coders proposed in literature. LPC at bit rates 600 BPS were given
by Kang et al [12]. Atal and Remde established a Multipulse Excitation Model and given the improvement
related to classical LPC, and then Self Excitated Vocoders and Residual Excited Linear Predictive (RELP)
coders were introduced. The encoded prediction residual in RELP is used to excite the synthesis filter. Speech
quality offered by RELP coders at 4.8 kbps is higher than that of two-state excited LPC coders. To remove the
annoying artifacts in LPC such as buzzes, tonal noises etc., the MELP uses sophisticated excitation and
improved filter model with additional parameters to capture signal dynamics with improved accuracy. MELP
utilized vector quantization for LSF parameters and achieved improvement in speech quality with naturalness,
smoothness and adaptability to diverse signal conditions, in comparison with 2.4 kbps LPC [13] without
elevating the bit rate.
3.3 Hybrid Coders
Hybrid coding combines strengths of waveform coding and parametric coding techniques. Like a
parametric coder, it relies on speech production model. Hybrid coders are used to encode speech, and the
bandwidth requirements lies between 4.8 and 16 kbps. The hybrid coders include CELP, MPE and RPE coders.
Multipulse Pulse Excited coding (MPE) and Regular Pulse Excited coding (RPE) techniques try to improve the
speech quality by giving a better representation of the excitation signal and it produces high quality speech at
9.6 kbps. Codebook Excited Linear Prediction (CELP) technique can be also called as analysis-by-synthesis
3. Secure Speech Communication - A Review For…
www.ijesi.org 7 | Page
(Abs) technique with the bit rates of 4.8 kbps. CELP and its variants are the most outstanding representatives of
this class which dominates medium bit rate coders. Idea of CELP was born as an attempt to improve on LPC
coder. It basically covered a wide range of bit rates 4.8-16 kbps.
IV. SPEECH ENCRYPTION AND DECRYPTION
Cryptography is the science of converting information from the comprehensible form to
incomprehensible form for its secured communication over the insecure channel. Encryption is a very common
technique for promoting the security and it is a much stronger method of protecting speech communication than
any form of scrambling. The various advantages of encryption includes that [14] it can protect information
stored on the computer from unauthorized access and while it is in transit from one computer system to another
it can protect information. In general, encryption techniques are classified into two broad categories [15] such as
Symmetric and Asymmetric Encryption
4.1 Symmetric Encryption
The symmetric encryption also called as single-key encryption, one-key encryption or private key encryption. It
uses the same secret key to encrypt and decrypt the information. It is essential that the sender and the receiver
should know the secret key, which will be helpful to encrypt and decrypt all the information’s. Fastness is the
major advantage of using symmetric encryption. Some of the commonly used symmetric encryption algorithms
are listed in table 2[16]
4.2 Asymmetric Encryption
In asymmetric encryption, different keys can be used to encrypt and decrypt the data. The encryption
key is public whereas the decryption key is private. However, it has two major disadvantages such as it is based
on nontrivial mathematical computations, and it is very slower than the symmetric ones. Some of the popular
examples of asymmetric encryption algorithm include RSA, DSA and PGP [17].The RSA encryption is the best
known public key
Symmetr
ic
Encrypti
on
Algorith
m
Developer Block
size
Cryptanalysis
resistance
Security
Advanced
Encryptio
n
Standard
Vincent
Rijmen
and Joan
Daemen in
2000
128-,
192- or
256-bit
It is very Strong
against truncated
differential,
linear,
interpolation and
square attacks
More secure
Data
Encryptio
n
Standard
IBM in
1977
64bit
block
Vulnerable to
differential and
linear crypt
analysis
Proven
inadequate
Triple
Data
Encryptio
n
Standard
1978 64bit
block
Vulnerable to
differential, Brute
force attacker
could be analyze
plain text using
differential crypt
analysis
One only
weak which
is exit in
DES
CAST Carlisle
Adams
and
Stafford
Tavares
in1996
64bit
block
Very fast and
efficient [18]
algorithm, named after its investors: Rivest, Shamir and Adleman. The key used for encryption is a public key
and the key used for decryption is a private key. The Digital Signature Algorithm (DSA) is a United States
Federal Government standard or FIPS for digital signatures. It was proposed by the National Institute of
Standards and Technology (NIST) in August 1991 for use in their Digital Signature Standard (DSS). PGP stands
for Pretty Good Privacy which is a public-private key cryptography system allows for users to easily integrate
the use of encryption in their daily tasks, such as electronic mail protection and authentication, and protecting
files stored on a computer. It was originally designed by Phil Zimmerman. It uses idea, cast or triple des for
actual data encryption and RSA (with up to 2048- bit key) or DH/DSS (with 1024-bit signature key and 4096-bit
encryption key) for key management and digital signatures.
V. REVIEW OF LITERATURE
W.W Chang et al., in the “automated cryptanalysis of DFT-based speech scramblers” [19] presented an
automated method for cryptanalysis of DFT based analog speech scramblers through statistical estimation
treatment. E.V.Stansfield et al in Speech processing techniques for HF radio security [20] explainsthe techniques
used to provide secure conversational speech communications over HF radio channel. An efficient
implementation of multi-prime on DSP Processor proposed by K. Anand etal in 2003[21] implemented
4. Secure Speech Communication - A Review For…
www.ijesi.org 8 | Page
Montgomery squaring reduction method which speed ups by 10.15% for various key sizes Texas Instrument
TMS320C6201 DSP Processor.Cryptanalysis of adaptive arithmetic coding encryption scheme presented by J
Lim et al in 1997[22] carried an analysis on different plaintext and cipher text and subsequent results were
evaluated accordingly. Speaker Recognition from Coded Speech and the Effects of Score Normalization was
proposed by R.B. Dunn et al [23]. This paper explains about the effect of speech coding on automatic speaker
recognition where training and testing conditions are matched and mismatched. In the recognition performance
there is little loss in the toll quality speech coders and more loss when lower quality speech coders are used.
Both types of score normalization considerably improve performance, and it can eliminate the performance loss
when there is a mismatch between training and testing conditions.
Jan Silovsky et al presented their work in the paper titled Assessment of Speaker Recognition on Lossy
Codecs used for Transmission of Speech in 2011[24].This paper investigates the effect of lossy codecs used in
telephony on text-independent speaker recognition. Here the speaker recognition performance is degraded due
to the bandwidth usage, transmission packet loss and utilization of discontinuous transmission techniques. There
is only little loss in recognition performance for codecs operating at bit rates of about 15 kb/s and the best
overall performance was observed for the SILK codec. R.B. Dunn et al present their work on” Speaker
Recognition from coded Speech and the Effects of Score Normalization”, in [25] MIT Lincoln Laboratory,
Lexington. The author investigates the effect of speech coding on automatic speaker recognition when training
and testing conditions are matched and mismatched. This paper use standard speech coding algorithms (GSM,
G.729, G.723, MELP) and a speaker recognition system based on gaussian mixture models adapted from a
universal background model for experimentation.
Aman Chadha, Divya Jyoti, M. Mani Roja, reviewed their work in the paper titled” Text-Independent
Speaker Recognition for Low SNR Environments with Encryption” in [26]. The main objective of this paper is
to implement a robust and secure voice recognition system using minimum resources offering optimum
performance in noisy environments. The proposed text-independent voice recognition system makes use of
multilevel cryptography to preserve data integrity while in transit or storage. The experimental results show that
the proposed algorithm can decrypt the signal under test with exponentially reducing Mean Square Error over an
increasing range of SNR. Further, it outperforms the conventional algorithms in actual identification tasks even
in noisy environments.A. R. Stauffer, A. D. Lawson Speaker Recognition on Lossy Compressed Speech using
the Speex Codec” in 2009 in[27]. This paper examines the impact of lossy speech coding with Speex on GMM-
UBM speaker recognition (SR). Results show that Speex is effective for compression of data used in SR and
that Speex coding can improve performance on data compressed by the GSM codec .J. D. Gibson, A. Servetti
focus on Selective Encryption and Scalable Speech Coding for Voice Communications over Multi-Hop Wireless
Links in [28]. This paper proposes and investigates a combination of scalable speech coding and selective
encryption for secure voice communication over multi-hop wireless links that addresses both the efficient use of
network, node resources and security against unwanted eavesdroppers. It is shown that when the Shannon lower
bound is satisfied with equality for rate distortion optimal scalable coding, transmission of the enhancement
layer in-the-clear provides no information regarding the core layer.
VI. PROPOSED METHODOLOGY
5. Secure Speech Communication - A Review For…
www.ijesi.org 9 | Page
Fig. 1 Secure Speech Communication
The overall proposed methodology for secured speech communication is given in figure 1 which is illustrated as
follows: o First of all the input speech signal is given by the sender.
But before transmitting to the receiver the speaker is identified by using the speech recognition algorithm to
prove that he is an authorized speaker.
Along with the speaker identification the original speech is compressed using the compression algorithm
As the next step the compressed speech is encrypted and the secure information is transmitted towards the
receiver through different channels
The receiver decompresses the data and decrypts the information to get the original speech and also he
checks by using the hashing algorithm that the original message came from the authorized user.
If the speaker identity matches then the receiver follows the instructions according to the sender o If there is
any mismatch the destination recipient will get alert.
VII. CONCLUSION AND FUTURE WORK
Speech processing for secured communication has been in development for more than 50 years. This
paper gives the various techniques in the field of Speech Coding, Speaker Identification with Encryption and
Decryption. The various approaches available for developing a secured communication are clearly explained. In
recent years, the need for secured communication research based on Speech Coding with cryptography has
highly increased. This paper is based on compression technologies that exploit this technology with encryption
and decryption for an environment that promotes and facilitates the use of safe communication. More
specifically, the application takes responsibility for the speaker identification using the cryptographic hash
functions so that the unauthorized speakers will find difficult to trace out the original data. The future work is to
find out the optimal method suitable for this environment to provide better secured communication.
REFERENCES
[1] A. Jameel et al, ” A robust secure speech communication system using ITU-T G.723.1 and TMS320C6711 DSP”,
Microprocessors and Microsystems, volume 30,2006,Pages 26-32
[2] A. Jameel, “Transform-domain and DSP based secure speech communication”, Microprocessors and Microsystems,2007, 335-
346
[3] http ://en.wikipedia. org/wiki/Crypto graphic hash function
[4] http://csrc.nist.gov/groups/ST/hash/sha- 3/index.html
6. Secure Speech Communication - A Review For…
www.ijesi.org 10 | Page
[5] Mark Hasegawa-Johnson et al,” Speech Coding: Fundamentals And Applications”, Wiley Encyclopedia of
Telecommunications
[6] Akella Amarendra Babu et al, ,”Robust speech processing in EW environment”, International Journal of Computer
Applications (0975 - 8887) Volume 38- No.11, January 2012
[7] Dr.V.Radha et al,” Comparative Analysis of Compression Techniques for Tamil Speech Datasets, IEEE, ICRTIT, June 3-5,
2011
[8] Venkatesh Krishnan,” A Framework For Low Bit-Rate Speech Coding In Noisy Environment”, A school of Electrical and
Computer Engineering
[9] P.Cummiskey et al,”Adaptive Quantization in Differential PCM Coding of speech, “Bell
sys.Tech.J.Vol.52.No.7.p.1105.sept.1973
[10] G.Kang and D.Coutler,”600 Bit-per-second voice digitizer(linear predictive format coder),”NRL Report 8043,Nov 1976
[11] Jorgen Ahlberg,” Speech & Audio Coding” TSBK01 Image Coding and Data Compression Lecture 11, 2003
[12] G.Kang,”Application of linear prediction to a narrow band voice digitizer,”NRL Report 7774,Oct.1974
[13] Benesty,Sondhi, Huang, “Springer Handbook of Speech Processing”
[14] Nehaluddin Ahmad,,”Privacy and the Indian Constitution: A case study of Encryption”, Communication of the IBIMA volume
7,2009 ISSN:1943-7765
[15] S.Rajanarayanan and A. Pushparaghavan, “Recent Developments in Signal Encryption - A Critical Survey”, International
Journal of Scientific and Research Publications, Volume 2, Issue 6, June 2012, ISSN 2250¬3153
[16] Hamdan.O.Alanazi etal, “New Comparative Study Between DES, 3DES and AeS within Nine Factors”, Journal of computing,
volume2, issue3, march 2010
[17] http://zybersene.blo spot.in/2012/06/symme tric-encryption-vs-asymmetric.html
[18] Http://www.omnisecu.com/security/public- key-infrastructure/symmetric-encryption- algorithms.htm
[19] W.W Chang et al., ”The automated cryptanalysis of DFT-based speech scramblers,” IEICE Trans .Information and
system,E83,pp.2107-2112,2000
[20] E.V Stansfield et al., “Speech processing techniques for HF radio security,” IEEE Proce.,vol.136,1989
[21] K.Anand,et al.,”An efficient implementation of multi-prime onDSP rocessor,”inProc.ICAS SP,2003,pp.413-416
[22] J.Lim et al.,”Cryptanalysis of adaptive arithmetic coding encryption scheme,” in Proc. ACISP,1997 pp.216-227
[23] R.B. Dunn, T.F. et al, “Speaker Recognition from Coded Speech and the Effects of Score Normalization”, IT Lincoln
Laboratory, Lexington, MA
[24] Jan Silovsky, Petr Cerva, Jindrich Zdansky,”Assessment of Speaker Recognition on Lossy Codecs Used for Transmission of
Speech”, 53rd International Symposium ELMAR-2011, 14-16 September , Zadar, Croatia
[25] R.B. Dunn, T.F. Quatieri, D.A. Reynolds,
J.P. Campbell, “Speaker Recognition from Coded Speech and the Effects of Score Normalization”, IT Lincoln Laboratory,
Lexington, MA
[26] Aman Chadha, Divya Jyoti, M. Mani Roja,
’’Text-Independent Speaker Recognition for Low SNR Environments with Encryption”,
International Journal of Computer Applications (0975 - 8887) Volume 31- No.
10, October 2011
[27] A. R. Stauffer, A. D. Lawson,” Speaker Recognition on Lossy Compressed Speech using the Speex Codec” 2009 ISCA 6-10
September, Brighton UK
[28] J. D. Gibson, A. Servetti ,” Selective Encryption and Scalable Speech Coding for Voice Communications over Multi-Hop
Wireless Links “