Asr
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The document discusses automatic speech recognition (ASR), including its definition, capabilities, uses, and history. It describes the multilayer structure of speech production and how ASR systems work, involving speech databases, feature extraction using MFCC, training hidden Markov models, and recognizing speech using the trained models. Modern ASR systems combine technologies from several fields to achieve continuous speech recognition.
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Asr
Automatic speech recognition (ASR) systems convert spoken words to text. ASR systems have a speech database for training, extract acoustic features like MFCCs from speech, and use hidden Markov models trained on large datasets to recognize speech in real-time. ASR has applications in dictation, command and control, telephony, and assisting those with disabilities. The timeline of ASR shows steady improvements from isolated word recognition to today's systems that can understand continuous speech.
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Credits to respective owners.
Automatic Speaker Recognition system using MFCC and VQ approach
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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.
The Main Concepts of Speech Recognition
The document provides an overview of the main concepts in speech recognition systems, including the lexicon, acoustic model, language model, and WFST decoder. It explains that the lexicon maps words to phone sequences, the acoustic model identifies pronunciations from audio features using deep neural networks, and the language model provides word probability distributions. It describes how the WFST decoder integrates these components by decoding speech as a path through a weighted finite state transducer to arrive at the most likely transcription.
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This document describes how to build a simple automatic speaker recognition system. It discusses the principles of speaker recognition, which can be identification (determining which registered speaker is speaking) or verification (accepting or rejecting a speaker's claimed identity). The key components are feature extraction and feature matching. Feature extraction converts the speech waveform into features using techniques like MFCC. Feature matching then compares the extracted features to stored reference models to identify the speaker. The document focuses on the speech feature extraction process, which involves framing the speech signal, windowing frames, taking the FFT, and calculating MFCCs to characterize the signal in a way that mimics human hearing.
Mini Project- Audio Enhancement
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Asr
Automatic speech recognition (ASR) systems convert spoken words to text. ASR systems have a speech database for training, extract acoustic features like MFCCs from speech, and use hidden Markov models trained on large datasets to recognize speech in real-time. ASR has applications in dictation, command and control, telephony, and assisting those with disabilities. The timeline of ASR shows steady improvements from isolated word recognition to today's systems that can understand continuous speech.
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The document provides an overview of the main concepts in speech recognition systems, including the lexicon, acoustic model, language model, and WFST decoder. It explains that the lexicon maps words to phone sequences, the acoustic model identifies pronunciations from audio features using deep neural networks, and the language model provides word probability distributions. It describes how the WFST decoder integrates these components by decoding speech as a path through a weighted finite state transducer to arrive at the most likely transcription.
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The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.Voice morphing-
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And an accurate copy of a person's voice can be made that can wishes to say, anything in the voice of someone else.
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Currently, the Yandex Speech Group provides three different services in Russian: maps, navigation, and general search, with a performance that is comparable to competitor products.
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LPC is a speech compression technique that models speech production as a linear function of past speech samples. It has an analysis stage that determines filter coefficients to reproduce a block of speech from the input signal. In the decoding stage, the filter is rebuilt using the coefficients. The document analyzes cepstral coefficients of male and female voices for different Bodo vowels, finding distinctions between genders for certain frames. It concludes LPCC measures may be better for identifying a speaker's sex.
Voice morphing
Voice morphing is a technique that modifies a source speaker's speech to sound like a target speaker. It works by preprocessing the speech signals, analyzing pitch and envelope, matching and warping the signals, and re-estimating the signal. This allows for an accurate copy of a person's voice to be made. Voice morphing has applications in text-to-speech systems, public address systems, and special effects. It can also be used to create fake conversations for use in courts or as a battlefield deception tool. The process separates speech into spectral envelope and pitch/voicing information before realigning and recombining the signals.
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This document describes how to build a simple, yet complete and representative automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.
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Voice morphing document
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by analyzing the source speech into an excitation signal and filter components, then resynthesizing it with the pitch and vocal characteristics of the target speaker. The key steps are detecting the pitches of the source and target speakers, scaling the source pitch to match the target, then resynthesizing the source speech using the target's vocal filter characteristics and the pitch-scaled excitation signal. Voice morphing was developed in 1999 and has applications in text-to-speech, dubbing, voice disguising, and public announcement systems.
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39 Surah Az Zumar (The Troops)
The document is a passage from the Quran that discusses several religious topics:
- It establishes that sincere obedience is only due to Allah alone.
- Allah has created humanity, animals, day and night, heavens and earth.
- On the Day of Judgment, those who were ungrateful and disobeyed Allah will be punished with fire, while the righteous will be rewarded.
- Believers are commanded to worship Allah alone and avoid idolatry.
31 Surah Luqman (Luqman)
The document is a summary of verses 31:1-23 from the Quranic chapter of Surah Luqman. It discusses several topics:
- The wisdom and guidance provided in the verses of the Quran.
- Admonitions from Luqman to his son to be grateful to God, avoid polytheism and idolatry, and establish prayers.
- God's creation of the heavens, earth, mountains and living creatures.
- Those who believe and do good works will receive gardens of bliss as reward.
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How speech reorganization works
Speech recognition works by converting analog audio signals from a microphone into digital data that is then analyzed using various mathematical and statistical techniques. The digital audio passes through different stages where it is converted into discrete frequency bands using FFT and then compared to a database of phonemes to identify the sounds. It uses techniques like hidden Markov models, tri-phones, and pruning to handle variations in sounds and identify when one phoneme ends and the next begins in order to translate speech into recognized text.
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Mini Project- Audio EnhancementUniversity of Hertfordshire, School of Electronic Communications and Electrical Engineering
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0073) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.Voice morphing-
It is a technique to modify a source speaker's speech to sound as if it was spoken by a target speaker.
Voice morphing enables speech patterns to be cloned
And an accurate copy of a person's voice can be made that can wishes to say, anything in the voice of someone else.
"Automatic speech recognition for mobile applications in Yandex" — Fran Campi...
This talk describes the work developed by the Yandex Speech Group in the last two years. Beginning from scratch, large amounts of voice recordings were collected from the field of application, and the most popular open source speech projects were studied to get a thorough understanding of the problem and to gather ideas to build our own technology. This talk will present key experiments and their results, as well as our latest achievements in automatic speech recognition in Russian.
Currently, the Yandex Speech Group provides three different services in Russian: maps, navigation, and general search, with a performance that is comparable to competitor products.
Speaker recognition systems
This presentation consists of description about speaker verification system and speaker identification system
Speaker Recognition System using MFCC and Vector Quantization Approach
This paper presents an approach to speaker recognition using frequency spectral information with Mel frequency for the improvement of speech feature representation in a Vector Quantization codebook based recognition approach. The Mel frequency approach extracts the features of the speech signal to get the training and testing vectors. The VQ Codebook approach uses training vectors to form clusters and recognize accurately with the help of LBG algorithm.
LPC for Speech Recognition
LPC is a speech compression technique that models speech production as a linear function of past speech samples. It has an analysis stage that determines filter coefficients to reproduce a block of speech from the input signal. In the decoding stage, the filter is rebuilt using the coefficients. The document analyzes cepstral coefficients of male and female voices for different Bodo vowels, finding distinctions between genders for certain frames. It concludes LPCC measures may be better for identifying a speaker's sex.
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Presentation slides discussing the theory and empirical results of a text-independent speaker verification system I developed based upon classification of MFCCs. Both mininimum-distance classification and least-likelihood ratio classification using Gaussian Mixture Models were discussed.
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Voice recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
This document describes how to build a simple, yet complete and representative automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.
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This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM, which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper
Voice morphing document
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by analyzing the source speech into an excitation signal and filter components, then resynthesizing it with the pitch and vocal characteristics of the target speaker. The key steps are detecting the pitches of the source and target speakers, scaling the source pitch to match the target, then resynthesizing the source speech using the target's vocal filter characteristics and the pitch-scaled excitation signal. Voice morphing was developed in 1999 and has applications in text-to-speech, dubbing, voice disguising, and public announcement systems.
Text independent speaker recognition system
This document outlines a project to develop a text-independent speaker recognition system. It lists the project members and provides an overview of the presentation sections, which include the system architecture, methodology, results and analysis, and applications. The methodology section describes implementing the system in MATLAB, including voice capturing, pre-processing, MFCC feature extraction, GMM matching, and identification/verification. It also outlines implementing the system on an FPGA, including analog conversion, storage, framing, FFT, mel spectrum, MFCC extraction, and UART transmission to MATLAB for further processing. The results show over 99% recognition accuracy with longer training and test data.
Hindi digits recognition system on speech data collected in different natural...
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acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
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The document discusses speech processing using MATLAB. It involves analyzing speech sounds through components like normalizers and segmenters, converting the sounds to a code, and then synthesizing speech by reproducing recorded elements. There are many applications ranging from limited to full communication systems. Speech signals are usually processed digitally as a form of digital signal processing. Aspects include acquiring, manipulating, storing, transferring and outputting speech signals.
2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning
The document discusses the creation of tangible learning objects using haptics and multimedia authoring tools. It describes a proposed multimedia authoring tool called HAMLAT that allows non-programmers to integrate haptic, visual and audio elements. An example application discussed is a multilingual handwriting learning and evaluation tool that provides haptic guidance. The tool uses dynamic time warping to measure and evaluate similarities between handwritten letters. Several case studies and demonstrations are proposed to verify the feasibility of identifying patterns with haptics and developing toddler learning interfaces.
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Just a discussion we had on Surah Ali-Imran by our Quranic Discourse group.
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The document is a summary of Surah al-Fajr (The Dawn) from the Quran. It recounts how God punished several past nations for their sins and corruption, including Ad, Thamud and Pharaoh. It warns that on the Day of Judgment, when the earth is crushed and God and his angels come, hell will be made visible and people will wish they had done more good in their lives. It calls the soul to return to God, pleased and pleasing, and enter paradise.
39 Surah Az Zumar (The Troops)
The document is a passage from the Quran that discusses several religious topics:
- It establishes that sincere obedience is only due to Allah alone.
- Allah has created humanity, animals, day and night, heavens and earth.
- On the Day of Judgment, those who were ungrateful and disobeyed Allah will be punished with fire, while the righteous will be rewarded.
- Believers are commanded to worship Allah alone and avoid idolatry.
31 Surah Luqman (Luqman)
The document is a summary of verses 31:1-23 from the Quranic chapter of Surah Luqman. It discusses several topics:
- The wisdom and guidance provided in the verses of the Quran.
- Admonitions from Luqman to his son to be grateful to God, avoid polytheism and idolatry, and establish prayers.
- God's creation of the heavens, earth, mountains and living creatures.
- Those who believe and do good works will receive gardens of bliss as reward.
21 Surah Al Anbiyaa (The Prophets)
The document is a passage from the Quran discussing several topics:
1) It warns that mankind will soon be called to account for their deeds while in a state of neglect.
2) It recounts how past nations rejected their prophets and demanded signs, yet were destroyed for their disbelief.
3) It affirms God's power over creation and that He has no partners or sons, in contrast to false gods worshipped by disbelievers.
18 Surah Al Kahf (The Cave)
The document summarizes the story of the youths who took refuge in a cave from their oppressive community. They prayed to God for guidance and fell asleep in the cave. God caused them to sleep for many years until they awoke, still young, and wondered how long they had slept. They sent one of their members into the town to inquire discreetly about the situation and bring back food. Their story is considered one of God's signs.
Artificial intelligence Speech recognition system
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
Voice Recognition
The document discusses voice recognition systems and their key components. It describes:
1) Sphinx, an open source tool used for speech recognition that uses Hidden Markov Models and applies feature extraction, language modeling, and acoustic modeling.
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3) Key parts of speech recognition systems including feature extraction, acoustic modeling, language modeling, and the use of HMMs to match features to models.
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The document outlines the main parts and structure of a speech to sign language interpreter system being developed, including:
1. The system uses automatic speech recognition to convert speech to text, then matches the text to pre-recorded ASL video clips from a database for translation.
2. The speech recognition engine uses a large vocabulary, speaker independent, continuous recognition model with components like signal processing, acoustic modeling, dictionaries, and language modeling.
3. The document also discusses sign languages like ASL and Signed English, and provides an example of the system's demonstration translating speech to matching ASL video clips or fingerspelling if no match is found.
SPEECH RECOGNITION USING NEURAL NETWORK
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 recognition techniques
The document discusses acoustic speech recognition techniques. It provides an introduction and historical survey, then describes the general model of ASR which involves feature extraction and hidden Markov models. It notes the development of early systems in the 1950s-1980s and more recent neural network approaches. The document advocates for expanding ASR systems to support local languages to improve human-machine interaction and information access for more people.
Speech recognition final presentation
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
50120140502007
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.
GENDER RECOGNITION SYSTEM USING SPEECH SIGNAL
In this paper, a system, developed for speech encoding, analysis, synthesis and gender identification is
presented. A typical gender recognition system can be divided into front-end system and back-end system.
The task of the front-end system is to extract the gender related information from a speech signal and
represents it by a set of vectors called feature. Features like power spectrum density, frequency at
maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT)
algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize
the gender from his/her speech signal in recognition phase. This paper also presents the digital processing
of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and
the rest of them are Female. Power Spectrum Estimation of the signal is examined .The frequency at
maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses
threshold technique as identification tool. The recognition accuracy of this system is 80% on average.
An Introduction To Speech Recognition
An Introduction To Speech RecognitionDepartment of Telecommunications, Ministry of Communication & IT (INDIA)
This document provides an overview of speech recognition including:
- The topics that will be covered such as speech production, why speech recognition is difficult, and applications.
- How speech is produced through the lungs, larynx, and vocal tract and modified into different sounds.
- The main components of a speech recognition system including sound sampling, conversion to frequencies, and matching to a phoneme database.
- Some of the challenges in speech recognition including variations between speakers and dependence on neighboring sounds.A Novel Method for Speaker Independent Recognition Based on Hidden Markov Model
In this paper, we address the speaker independent
recognition of Chinese number speeches 0~9 based on HMM.
Our former results of inside and outside testing achieved
92.5% and 76.79% respectively. To improve further the
performance, two important features of speech; MFCC and
cluster number of vector quantification, are unified together
and evaluated on various values. The best performance
achieve 96.2% and 83.1% on MFCC Number = 20 and VQ
clustering number = 64.
Modeling of Speech Synthesis of Standard Arabic Using an Expert System
This document describes an expert system for speech synthesis of Standard Arabic text. It involves two main stages: 1) creation of a sound database and 2) text-to-speech transformation. The transformation process involves phonetic orthographic transcription of the text and then generating voice signals corresponding to the transcribed phonetic sequence. The expert system uses a knowledge base containing sound data and rewriting rules. It transcribes text using graphemes as basic units and then concatenates sound units from the database to synthesize speech. Tests achieved a 96% success rate in pronouncing sentences correctly. Future work aims to improve prosody and develop fully automatic signal segmentation.
Utterance Based Speaker Identification Using ANN
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 ANN
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.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition Technique
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
A comparison of different support vector machine kernels for artificial speec...
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.
Realization and design of a pilot assist decision making system based on spee...
A system based on speech recognition is proposed fo
r pilot assist decision-making. It is based
on a HIL aircraft simulation platform and uses the
microcontroller SPCE061A as the central
processor to achieve better reliability and higher
cost-effect performance. Technologies of
LPCC (linear predictive cepstral coding) and DTW (D
ynamic Time Warping) are applied for
isolated-word speech recognition to gain a smaller
amount of calculation and a better real-time
performance. Besides, we adopt the PWM (Pulse Width
Modulation) regulation technology to
effectively regulate each control surface by speech
, and thus to assist the pilot to make decisions.
By trial and error, it is proved that we have a sat
isfactory accuracy rate of speech recognition
and control effect. More importantly, our paper pro
vides a creative idea for intelligent human-
computer interaction and applications of speech rec
ognition in the field of aviation control. Our
system is also very easy to be extended and applied
Voice Recognition System using Template Matching
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.
Comparison and Analysis Of LDM and LMS for an Application of a Speech
Most of the automatic speech recognition (ASR) systems are based on Guassian Mixtures model. The output of these models depends on subphone states. We often measure and transform the speech signal in another form to enhance our ability to communicate. Speech recognition is the conversion from acoustic waveform into written equivalent message information. The nature of speech recognition problem is heavily dependent upon the constraints placed on the speaker, speaking situation and message context. Various speech recognition systems are available. The system which detects the hidden conditions of speech is the best model. LMS is one of the simple algorithm used to reconstruct the speech and linear dynamic model is also used to recognize the speech in noisy atmosphere..This paper is analysis and comparison between the LDM and a simple LMS algorithm which can be used for speech recognition purpose.
Utterance based speaker identification
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.
Investigation of-combined-use-of-mfcc-and-lpc-features-in-speech-recognition-...
This document discusses a study investigating the combined use of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) features in automatic speech recognition systems. It begins by outlining the challenges of automatic speech recognition and then describes the MFCC and LPC algorithms for extracting basic speech features. The study suggests combining MFCC and LPC-based recognition subsystems to improve reliability. Neural networks are used for training and recognition, and results show the combined approach improves recognition quality compared to individual methods.
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Wreck a nice beach: adventures in speech recognition
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A Novel Method for Speaker Independent Recognition Based on Hidden Markov Model
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A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition Technique
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition Technique
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Asr
- 1. "And of knowledge, you (mankind) have been given only a little." Surah Al-Isra‘, verse 85.
- 8. A Timeline & History of Voice Recognition Software Dragon released discrete word dictation-level speech recognition software. It was the first time dictation speech & voice recognition technology was available to consumers . 1995 SpeechWorks, the leading provider of over-the-telephone automated speech recognition (ASR) solutions, was founded. 1984 Dragon Systems was founded. 1982 DARPA established the Speech Understanding Research (SUR) program. A $3 million per year of government funds for 5 years. It was the largest speech recognition project ever. 1971 HMM approach to speech & voice recognition was invented by Lenny Baum of Princeton University Early 1970's AT&T's Bell Labs produced the first electronic speech synthesizer called the Voder. 1936
- 9. … timeline…continue Scansoft, Inc. is presently the world leader in the technology of Speech Recognition in the commercial market. ScanSoft Ships Dragon NaturallySpeaking 7 Medical, Lowers Healthcare Costs through Highly Accurate Speech Recognition. 2003 Lernout & Hauspie acquired Dragon Systems for approximately $460 million. 2000 Microsoft invested $45 million to allow Microsoft to use speech & voice recognition technology in their systems. 1998 Dragon introduced "Naturally Speaking", the first "continuous speech" dictation software available 1997
- 10. The Structure of ASR System: Functional Scheme of an ASR System Speech samples X Y S W * Database Signal Interface Feature Extraction Recognition Databases Training HMM
- 13. Many databases are distributed by the Linguistic Data Consortium www.ldc.upenn.edu
- 14. Speech Signal Analysis Feature Extraction for ASR: - The aim is to extract the voice features to distinguish different phonemes of a language.