This document provides an overview of speech processing. It begins with prerequisites in signals and systems, digital signal processing, and advanced DSP. It then introduces speech production and perception systems, representing speech in time and frequency domains. Speech sounds are classified, including vowels, consonants, fricatives, and plosives. Spectrograms and the spectral envelope are discussed for representing speech signals. Automatic speech processing is complicated due to its interdisciplinary nature, requiring signal processing, physics, pattern recognition, linguistics, and other areas.
This document discusses different types of teleconferencing including audio, video, and business television conferencing. It describes the advantages of teleconferencing such as saving time and costs while allowing larger audiences. Video conferencing allows participants to see each other through the use of cameras and displays. It has become more common recently due to higher bandwidth availability. Both audio and video conferencing can be used for presentations, meetings, learning and project coordination but have limitations such as high equipment costs and technical issues.
This document describes the process of creating a large-scale audio-visual dataset of celebrity speakers from YouTube videos, called VoxCeleb. Face detection and tracking were used to extract audio segments where a detected face was speaking. Face verification then identified which celebrity the face matched. Over 1,200 identities were included, with over 100,000 video clips extracted through an automated pipeline. The dataset enables research in audio-visual speech recognition and speaker identification in unconstrained conditions.
Current Trends in Signal Processing vol 6 issue 3STM Journals
Current Trends in Signal Processing ( CTSP) is a print and e-journal focused towards the rapid publication of fundamental research papers on areas of Signal Processing.
Focus and Scope Covers
Electrical Engineering and Signaling
Systems Engineering and Signaling
Applied Mathematics and Signaling
Control System Signals
Telecommunication Transmission Signals
Analog & Digital Signals
Time Varying Measurement Values & Sensor Data
The document discusses mobile phone jammers, which transmit signals to block cellular communication. It provides a brief history of their development for law enforcement and military use. The document then explains how jammers work by overpowering cell frequencies. It outlines different jamming techniques, including types that transmit interfering signals or target control channels. It also covers the design parameters, components like the power supply, and applications of jammers to maintain silence in places like libraries, schools and hospitals. Finally, it discusses the future potential of passive blocking and concludes that while the technology has risks, it can benefit society when used carefully.
This document discusses traffic and road safety issues in India. It notes that India has a diverse range of vehicles on its roads from slow moving rickshaws to fast motorbikes. While laws exist, traffic safety remains poor due to factors like lack of separate lanes, speeding vehicles, and lack of enforcement. It provides statistics on the high number of traffic accidents and deaths in India each year, with most caused by driver error or mechanical defects. Overall, it argues that India faces serious road safety challenges due to insufficient infrastructure, disrespect between road users, and lack of priority given to improving the situation.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Head-Mounted Display Visualizations to Support Sound Awareness for the Deaf a...Dhruv Jain
These are the slides for my talk at CHI 2015 in Seoul, Korea April 18-23. I performed this work while working as a research assistant in the Human-Computer Interaction Laboratory (HCIL) at the University of Maryland, College Park under the supervision of Profs. Leah Findlater and Jon Froehlich. Here is the link to the powerpoint slides: http://dhruvjain.info/me/media/GlassEar/GlassEar_CHI2015Talk_v6.7_UPLOADED.pptx and the YouTube video: https://www.youtube.com/watch?v=2jwWHcQv0s8.
ABSTRACT: Persons with hearing loss use visual signals such as gestures and lip movement to interpret speech. While hearing aids and cochlear implants can improve sound recognition, they generally do not help the wearer localize sound necessary to leverage these visual cues. In this paper, we design and evaluate visualizations for spatially locating sound on a head-mounted display (HMD). To investigate this design space, we developed eight high-level visual sound feedback dimensions. For each dimension, we created 3-12 example visualizations and evaluated these as a design probe with 24 deaf and hard of hearing participants (Study 1). We then implemented a real-time proof-of-concept HMD prototype and solicited feedback from 4 new participants (Study 2). Study 1 findings reaffirm past work on challenges faced by persons with hearing loss in group conversations, provide support for the general idea of sound awareness visualizations on HMDs, and reveal preferences for specific design options. Although preliminary, Study 2 further contextualizes the design probe and uncovers directions for future work.
This document discusses the technology of voice morphing. It begins by explaining that voice morphing allows modification of a person's voice in real-time by altering pitch, tone, and other characteristics. It then covers several key techniques used in voice morphing, including speech signal processing techniques like filtering, modulation, and transformation. Vocal tract modeling and the source-filter model are also summarized. The document concludes by discussing applications of voice morphing in entertainment, security, and healthcare and thanking the reader.
This document discusses different types of teleconferencing including audio, video, and business television conferencing. It describes the advantages of teleconferencing such as saving time and costs while allowing larger audiences. Video conferencing allows participants to see each other through the use of cameras and displays. It has become more common recently due to higher bandwidth availability. Both audio and video conferencing can be used for presentations, meetings, learning and project coordination but have limitations such as high equipment costs and technical issues.
This document describes the process of creating a large-scale audio-visual dataset of celebrity speakers from YouTube videos, called VoxCeleb. Face detection and tracking were used to extract audio segments where a detected face was speaking. Face verification then identified which celebrity the face matched. Over 1,200 identities were included, with over 100,000 video clips extracted through an automated pipeline. The dataset enables research in audio-visual speech recognition and speaker identification in unconstrained conditions.
Current Trends in Signal Processing vol 6 issue 3STM Journals
Current Trends in Signal Processing ( CTSP) is a print and e-journal focused towards the rapid publication of fundamental research papers on areas of Signal Processing.
Focus and Scope Covers
Electrical Engineering and Signaling
Systems Engineering and Signaling
Applied Mathematics and Signaling
Control System Signals
Telecommunication Transmission Signals
Analog & Digital Signals
Time Varying Measurement Values & Sensor Data
The document discusses mobile phone jammers, which transmit signals to block cellular communication. It provides a brief history of their development for law enforcement and military use. The document then explains how jammers work by overpowering cell frequencies. It outlines different jamming techniques, including types that transmit interfering signals or target control channels. It also covers the design parameters, components like the power supply, and applications of jammers to maintain silence in places like libraries, schools and hospitals. Finally, it discusses the future potential of passive blocking and concludes that while the technology has risks, it can benefit society when used carefully.
This document discusses traffic and road safety issues in India. It notes that India has a diverse range of vehicles on its roads from slow moving rickshaws to fast motorbikes. While laws exist, traffic safety remains poor due to factors like lack of separate lanes, speeding vehicles, and lack of enforcement. It provides statistics on the high number of traffic accidents and deaths in India each year, with most caused by driver error or mechanical defects. Overall, it argues that India faces serious road safety challenges due to insufficient infrastructure, disrespect between road users, and lack of priority given to improving the situation.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Head-Mounted Display Visualizations to Support Sound Awareness for the Deaf a...Dhruv Jain
These are the slides for my talk at CHI 2015 in Seoul, Korea April 18-23. I performed this work while working as a research assistant in the Human-Computer Interaction Laboratory (HCIL) at the University of Maryland, College Park under the supervision of Profs. Leah Findlater and Jon Froehlich. Here is the link to the powerpoint slides: http://dhruvjain.info/me/media/GlassEar/GlassEar_CHI2015Talk_v6.7_UPLOADED.pptx and the YouTube video: https://www.youtube.com/watch?v=2jwWHcQv0s8.
ABSTRACT: Persons with hearing loss use visual signals such as gestures and lip movement to interpret speech. While hearing aids and cochlear implants can improve sound recognition, they generally do not help the wearer localize sound necessary to leverage these visual cues. In this paper, we design and evaluate visualizations for spatially locating sound on a head-mounted display (HMD). To investigate this design space, we developed eight high-level visual sound feedback dimensions. For each dimension, we created 3-12 example visualizations and evaluated these as a design probe with 24 deaf and hard of hearing participants (Study 1). We then implemented a real-time proof-of-concept HMD prototype and solicited feedback from 4 new participants (Study 2). Study 1 findings reaffirm past work on challenges faced by persons with hearing loss in group conversations, provide support for the general idea of sound awareness visualizations on HMDs, and reveal preferences for specific design options. Although preliminary, Study 2 further contextualizes the design probe and uncovers directions for future work.
This document discusses the technology of voice morphing. It begins by explaining that voice morphing allows modification of a person's voice in real-time by altering pitch, tone, and other characteristics. It then covers several key techniques used in voice morphing, including speech signal processing techniques like filtering, modulation, and transformation. Vocal tract modeling and the source-filter model are also summarized. The document concludes by discussing applications of voice morphing in entertainment, security, and healthcare and thanking the reader.
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
Presentation regarding development of text-to-speech system for Gujarati. Input would be arbitrary Gujarati unicode text while output would equivalent speech sound.
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.
Speech recognition and digital image processing.pptxRakeshR458516
A speech processing system has various applications including analysis, transmission, reception of audio as in radio/TV/phone, noise removal, compression, speaker identification and verification, speech synthesis, and voice to text conversion. Speech is produced through vibration of vocal cords exciting the vocal tract for voiced sounds or forcing air through constrictions creating turbulence for unvoiced sounds. A speech production model represents this mechanism using a pulse train generator for voiced speech, noise generator for unvoiced, and a vocal tract system that outputs the synthesized speech signal.
Enterprise Voice Technology Solutions: A PrimerCognizant
The document provides an overview of enterprise voice technology solutions, including interactive voice response (IVR) applications, dictation applications, voice biometrics, and speech analytics. It describes the key components of voice applications, such as the automatic speech recognizer which uses acoustic models, dictionaries, and language models to convert speech to text. It emphasizes the importance of training these models for accurate speech recognition. Finally, it recommends three initial steps for enterprises looking to adopt voice technology: choosing the right product partner and solutions integrator, and preparing for iterative training and tuning of the voice solution.
This document discusses applications of artificial intelligence technologies in web services. It provides examples of speech recognition, which converts speech to text, and speech synthesis, which converts text to speech. For speech recognition, it explains the stages of acoustics, language processing, and implementation. For speech synthesis, it outlines the process of converting text to words, words to phonemes, and generating sounds from phonemes. Overall, the document explores how AI can make web services more user-friendly and responsive through technologies like speech and language processing.
The document discusses effective presentation design using multimedia. It covers basic concepts like choosing media based on the message, types of media including text, images and audio. It also discusses aspects of designing presentations like sequencing, exploration and indexed design. Media attributes like realism, interactivity and bandwidth are covered. Different tools for preparing presentations like PowerPoint, Liquid Media and WebProse are also mentioned.
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Voice-over translation services synchronize foreign audio content with video by converting the audio clip into the target language. These services are commonly used in industries like film and advertising to make audiovisual content accessible to international audiences. When choosing an agency, factors to consider include the languages spoken by voice actors, accepted file formats, and certification of translators. The process involves submitting the file, translating and recording the voice-over, and delivering the final file. The time and cost depend mainly on the length of the clip and number of voice actors required. PEC Translations offers professional voice-over talents, certified translations, and latest recording technology to provide high quality voice-over translation services.
The document discusses communication in instructional design. It covers the importance of communication with clients and understanding their needs and goals. The core ADDIE process model is introduced as a framework for analyzing client and audience needs to design effective instruction. Communication skills, attitudes, knowledge of the client's social systems and culture are important to gather the right information.
This document provides an outline and details of a student internship project on text-to-speech conversion using the Python programming language. The project was conducted at iPEC Solutions, which provides AI training and services. The student designed a text-to-speech system using tools including Praat, Audacity, and WaveSurfer. The system converts text to speech by extracting phonetic components, matching them to inventory items, and generating acoustic signals for output. The project aimed to help those with communication difficulties through improved accessibility of text-to-speech technology.
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.
Combined feature extraction techniques and naive bayes classifier for speech ...csandit
Speech processing and consequent recognition are important areas of Digital Signal Processing
since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing
speech, features are to be ex-tracted from speech and hence feature extraction method plays an
important role in speech recognition. Here, front end processing for extracting the features is
per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and
Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.
After classification using Naive Bayes classifier, DWT produced a recognition accuracy of
83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new
feature extraction method which produces improvements in the recognition accuracy. So, a new
method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated
and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH ...cscpconf
Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays animportant role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.After classification using Naive Bayes classifier, DWT produced a recognition accuracy of83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
[DL輪読会]IMPROVING VOICE SEPARATION BY INCORPORATING END-TO-END SPEECH RECOGNITIONDeep Learning JP
1) The document proposes improving voice separation performance by incorporating end-to-end speech recognition neural networks trained on large speech datasets.
2) It finds that the phonetic and linguistic information learned by the end-to-end speech recognition model is beneficial for voice separation tasks, even when the quality of training data differs.
3) Evaluation on voice separation from noisy mixtures and singing voice separation with limited data finds the proposed method outperforms baselines and is robust to adverse noise conditions.
The document provides an overview of automatic speech recognition, including: describing the process of speech recognition which involves feature extraction from voice and using acoustic and language models; listing common types like speaker-dependent and independent; discussing applications in areas like dictation, in-car systems, and voice security; and noting both advantages like reducing errors but also challenges involving filtering noise and accommodating various speaking styles.
This document discusses speech recognition techniques. It begins by defining biometrics and how speech can be used as a biometric for identity authentication. It describes how speech recognition aims to extract lexical information independently of the speaker, while speaker recognition focuses on extracting the identity of the speaker. The document then discusses feature extraction using MFCC and modeling speech using neural networks. It provides an overview of pattern recognition techniques including statistical and structural approaches. Finally, it discusses implementation details such as preprocessing, framing, windowing and feature extraction of speech signals.
The document discusses video conferencing, defining it as enabling people in different locations to see and hear each other. It describes the basic components of video conferencing systems as cameras, microphones, speakers and monitors. The two main types are point-to-point between two locations and multipoint connecting more than two sites through a control unit. Some common uses are for business meetings, e-learning, presentations and telemedicine. Advantages include reduced costs, improved communication and accommodating different learning styles, while limitations are high initial costs and potential issues with blurred images or audio echoes.
Speechbird AI Review – Unleashing the Power of Speech Recognition.pdfAMB-Review
Speechbird AI Review - Unleashing the Power of Speech Recognition
https://www.amb-review.com/sbai
#SpeechBird AI
#Speech recognition
#Transcription made easy
#Multilingual support
#Accurate and reliable
#Time-saving tool
#Seamless user experience
#Adaptive machine learning
#Enhancing productivity
#Customer-centric support
Automated Voice And Audio Quality Test MeasurementSevana Oü
AQuA is a simple but powerful tool to provide perceptual voice quality testing and audio file comparison. This is the easiest way to compare two audio files and test sound signals quality between original and degraded files.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
Presentation regarding development of text-to-speech system for Gujarati. Input would be arbitrary Gujarati unicode text while output would equivalent speech sound.
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.
Speech recognition and digital image processing.pptxRakeshR458516
A speech processing system has various applications including analysis, transmission, reception of audio as in radio/TV/phone, noise removal, compression, speaker identification and verification, speech synthesis, and voice to text conversion. Speech is produced through vibration of vocal cords exciting the vocal tract for voiced sounds or forcing air through constrictions creating turbulence for unvoiced sounds. A speech production model represents this mechanism using a pulse train generator for voiced speech, noise generator for unvoiced, and a vocal tract system that outputs the synthesized speech signal.
Enterprise Voice Technology Solutions: A PrimerCognizant
The document provides an overview of enterprise voice technology solutions, including interactive voice response (IVR) applications, dictation applications, voice biometrics, and speech analytics. It describes the key components of voice applications, such as the automatic speech recognizer which uses acoustic models, dictionaries, and language models to convert speech to text. It emphasizes the importance of training these models for accurate speech recognition. Finally, it recommends three initial steps for enterprises looking to adopt voice technology: choosing the right product partner and solutions integrator, and preparing for iterative training and tuning of the voice solution.
This document discusses applications of artificial intelligence technologies in web services. It provides examples of speech recognition, which converts speech to text, and speech synthesis, which converts text to speech. For speech recognition, it explains the stages of acoustics, language processing, and implementation. For speech synthesis, it outlines the process of converting text to words, words to phonemes, and generating sounds from phonemes. Overall, the document explores how AI can make web services more user-friendly and responsive through technologies like speech and language processing.
The document discusses effective presentation design using multimedia. It covers basic concepts like choosing media based on the message, types of media including text, images and audio. It also discusses aspects of designing presentations like sequencing, exploration and indexed design. Media attributes like realism, interactivity and bandwidth are covered. Different tools for preparing presentations like PowerPoint, Liquid Media and WebProse are also mentioned.
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Voice-over translation services synchronize foreign audio content with video by converting the audio clip into the target language. These services are commonly used in industries like film and advertising to make audiovisual content accessible to international audiences. When choosing an agency, factors to consider include the languages spoken by voice actors, accepted file formats, and certification of translators. The process involves submitting the file, translating and recording the voice-over, and delivering the final file. The time and cost depend mainly on the length of the clip and number of voice actors required. PEC Translations offers professional voice-over talents, certified translations, and latest recording technology to provide high quality voice-over translation services.
The document discusses communication in instructional design. It covers the importance of communication with clients and understanding their needs and goals. The core ADDIE process model is introduced as a framework for analyzing client and audience needs to design effective instruction. Communication skills, attitudes, knowledge of the client's social systems and culture are important to gather the right information.
This document provides an outline and details of a student internship project on text-to-speech conversion using the Python programming language. The project was conducted at iPEC Solutions, which provides AI training and services. The student designed a text-to-speech system using tools including Praat, Audacity, and WaveSurfer. The system converts text to speech by extracting phonetic components, matching them to inventory items, and generating acoustic signals for output. The project aimed to help those with communication difficulties through improved accessibility of text-to-speech technology.
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.
Combined feature extraction techniques and naive bayes classifier for speech ...csandit
Speech processing and consequent recognition are important areas of Digital Signal Processing
since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing
speech, features are to be ex-tracted from speech and hence feature extraction method plays an
important role in speech recognition. Here, front end processing for extracting the features is
per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and
Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.
After classification using Naive Bayes classifier, DWT produced a recognition accuracy of
83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new
feature extraction method which produces improvements in the recognition accuracy. So, a new
method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated
and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH ...cscpconf
Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays animportant role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.After classification using Naive Bayes classifier, DWT produced a recognition accuracy of83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
[DL輪読会]IMPROVING VOICE SEPARATION BY INCORPORATING END-TO-END SPEECH RECOGNITIONDeep Learning JP
1) The document proposes improving voice separation performance by incorporating end-to-end speech recognition neural networks trained on large speech datasets.
2) It finds that the phonetic and linguistic information learned by the end-to-end speech recognition model is beneficial for voice separation tasks, even when the quality of training data differs.
3) Evaluation on voice separation from noisy mixtures and singing voice separation with limited data finds the proposed method outperforms baselines and is robust to adverse noise conditions.
The document provides an overview of automatic speech recognition, including: describing the process of speech recognition which involves feature extraction from voice and using acoustic and language models; listing common types like speaker-dependent and independent; discussing applications in areas like dictation, in-car systems, and voice security; and noting both advantages like reducing errors but also challenges involving filtering noise and accommodating various speaking styles.
This document discusses speech recognition techniques. It begins by defining biometrics and how speech can be used as a biometric for identity authentication. It describes how speech recognition aims to extract lexical information independently of the speaker, while speaker recognition focuses on extracting the identity of the speaker. The document then discusses feature extraction using MFCC and modeling speech using neural networks. It provides an overview of pattern recognition techniques including statistical and structural approaches. Finally, it discusses implementation details such as preprocessing, framing, windowing and feature extraction of speech signals.
The document discusses video conferencing, defining it as enabling people in different locations to see and hear each other. It describes the basic components of video conferencing systems as cameras, microphones, speakers and monitors. The two main types are point-to-point between two locations and multipoint connecting more than two sites through a control unit. Some common uses are for business meetings, e-learning, presentations and telemedicine. Advantages include reduced costs, improved communication and accommodating different learning styles, while limitations are high initial costs and potential issues with blurred images or audio echoes.
Speechbird AI Review – Unleashing the Power of Speech Recognition.pdfAMB-Review
Speechbird AI Review - Unleashing the Power of Speech Recognition
https://www.amb-review.com/sbai
#SpeechBird AI
#Speech recognition
#Transcription made easy
#Multilingual support
#Accurate and reliable
#Time-saving tool
#Seamless user experience
#Adaptive machine learning
#Enhancing productivity
#Customer-centric support
Automated Voice And Audio Quality Test MeasurementSevana Oü
AQuA is a simple but powerful tool to provide perceptual voice quality testing and audio file comparison. This is the easiest way to compare two audio files and test sound signals quality between original and degraded files.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
2. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Outline
Introduction
Human Speech Production and Perception Systems
Representation of Speech in the Time and Frequency
Domains
Speech Sounds and Features
Signal Processing Methods for Estimating Speech
Features
Speech Processing Applications
Speech Recognition
Speech Synthesis
Govind CEN, Amrita Vishwa Vidyapeetham
3. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Prerequisites: S&S, DSP & ADSP
Prior Knowledge Required:
Signals and Systems
Digital signal Processing
Advanced DSP
Govind CEN, Amrita Vishwa Vidyapeetham
4. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Prerequisites: S&S, DSP & ADSP
Signals and Systems
Classification of Signals
LTI systems
Correlation/Convolution Operations
Fourier Representation: FS, DTFS, DTFT,DFT,FFT,
Z-transform
Concepts of Impulse Response, Frequency Response etc.
Govind CEN, Amrita Vishwa Vidyapeetham
5. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Prerequisites: S&S, DSP & ADSP
Digital signal Processing
Sampling: Nyquist, Aliasing
FFT implementation of DFT
Design of FIR and IIR filters
Structures for realization of Filters
Multirate signal processing: Filter banks
Govind CEN, Amrita Vishwa Vidyapeetham
6. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Prerequisites: S&S, DSP & ADSP
Advanced DSP
Time-Frequency Analysis
TFA by STFT
TFA by wigner Distribututions
TFA by Wavelets
Govind CEN, Amrita Vishwa Vidyapeetham
7. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Prerequisites: S&S, DSP & ADSP
References
L. Rabiner, Biing-Hwang Juang and B.
Yegnanarayana,"Fundamentals of Speech
Recognition",Pearson Education Inc.2009
Douglas O’Shaughnessy,"Speech
Communication",University Press,2001
Thomas F Quatieri,"Discrete Time Speech Signal
Processing", Pearson Education Inc.,2004
Govind CEN, Amrita Vishwa Vidyapeetham
8. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Introduction
Information in Speech
Message
Language
Accent
Speaker
Emotions/Stress
Applications
Recognition
Speech recognition
Speaker Recognition/Verification
Emotion Recognition etc..
Synthesis
Text to Speech Synthesis
Speech Enhancement
Voice Conversion
Govind CEN, Amrita Vishwa Vidyapeetham
9. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Applications:Recognition
Speech Objective Information Extracted
Message Author of the danger...
Speaker Its Govind Speaking
Speaker claim has to
be verified
Hi Govind, your claim is ac-
cepted
Govind CEN, Amrita Vishwa Vidyapeetham
10. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Applications:Synthesis
Input Objective Output
Text To Speech Synthesis
Text (Epochs Occur... Synthesize Text
Speech Enhancement
Remove noise
Remove reverberation
Enhance desired
speaker speech
Voice Conversion
Convert source
speaker speech target
speakr speech
Govind CEN, Amrita Vishwa Vidyapeetham
11. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
What makes automatic processing of speech
Complicated?
Its an inter-disciplinary area
1 Signal Processing: The process of extracting relevant information from
speech signal
2 Physics: The science of understanding relationship between physical
speech signal and physiological mechanisms that produced it.
3 Pattern Recognition: Grouping or classifying patterns of various events
in speech
4 Communication and information theory: Deals with efficient way of
encodng or decoding parameters of speech, efficient serach for patterns of
interest in speech (dynamic programming, viterbi search, stack algorithms
etc..)
5 Linguistics: The relationship between sounds (phonology) with syntax
and semantics of a language and sense that derived from the meaning
(pragmatics)
6 Computer Science: The study of diferent algorithms for implementing in
Software/Hardware
7 Psychology: Understanding the psychological state of the
speaker/listener will be helpful for the tasks like emotion analysis.
Govind CEN, Amrita Vishwa Vidyapeetham
12. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Speaker-Listener Schematic Diagram in Speech
Communication
Figure: Schematic Diagram of Speech Communication: Figure
Courtesy- Rabiner et al.
Govind CEN, Amrita Vishwa Vidyapeetham
14. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Speech Production
Figure: Speech production mechanism: Figure Courtesy- Thomas F. Quatieri,
"Discrete-Time Speech Signal Processing", Chapter. 3, pp. 58, Pearson Edu., Delhi
Govind CEN, Amrita Vishwa Vidyapeetham
15. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Mechanical Equivalent of Speech Production System
Figure: Speech production mechanism: Figure Courtesy- Rabiner et
al.
Govind CEN, Amrita Vishwa Vidyapeetham
16. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Representation of Speech Signal
0 0.5 1 1.5 2 2.5
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
Figure: Speech Signal in Time domain
Govind CEN, Amrita Vishwa Vidyapeetham
17. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Glottal Air Flow During Speech Production
Figure: Glottal air flow: Courtesy- Rabinar et al.
Govind CEN, Amrita Vishwa Vidyapeetham
18. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Glottal Air Flow: Graphical Illustration
1.3 1.35 1.4 1.45 1.5 1.55
x 10
4
−1
−0.5
0
0.5
Time (Samples)
Amplitude
Speech Waveform
1.3 1.35 1.4 1.45 1.5 1.55
x 10
4
−1
−0.5
0
0.5
Time (Samples)
Amplitude
Glottal Flow: EGG
Speech EGG
Glottis
Vibration
Govind CEN, Amrita Vishwa Vidyapeetham
19. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Classification of Speech Sounds
Silence (S): No Speech is produced
Unvoiced (U): Vocal folds are not vibrating
Voiced (V): Periodic vibration of vocal cords
0 0.5 1 1.5 2 2.5
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
US S
V
V
V
Figure: Speech signal in time domainGovind CEN, Amrita Vishwa Vidyapeetham
20. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Classification of Speech Sounds
Separation of voiced sounds from unvoiced and silence
sounds is known as voiced-non-voiced detection
Issues in voiced-non-voiced detection:
Difficult to identify weak unvoiced sound from silence
Difficult to distinguish weakly periodic voiced sounds from
unvoiced sounds
Govind CEN, Amrita Vishwa Vidyapeetham
24. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Representation of sound units in speech
Sounds are classified into vowels and consonant
Vowels: By exciting fixed vocaltract shape with quasi
periodic glottal pulses
Vowels are classified into front, mid and back based on the
tongue-hump-position
Front vowels:/i/("eve"), /I/("it"),//("at"),/e/("hate")
Mid vowels: /a/("father"), /Λ/("Up")
Back Vowels: /U/("foot"),/u/("boot"),/o/("Obey")
Another classification is based on the length of vowels:
Long and short
Diphthongs: Combination of two vowels
/ay/ as in "buy",/aw/ as in "down",/ey/ as in "bait",/o/ as in
"boat",/cy/ as in "boy" etc.
Govind CEN, Amrita Vishwa Vidyapeetham
26. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Vowel Analysis
Front vowels found to show high frequency resonance
Front vowels are discriminated among each other by the
tongue height during the vowel production
Mid vowels found to show well separated and balanced
resonant frequency distribution
Back vowels shows almost no energy beyond low
frequency regions
Govind CEN, Amrita Vishwa Vidyapeetham
28. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Semivowels
Group of sounds consisting of /w/,/r/,/l/,/y/
difficult to characterize because they are vowel like in
nature
Characterized by gliding transition in vocaltract area
functions between adjacent phonemes
Best described as transitional vowel like sounds
Govind CEN, Amrita Vishwa Vidyapeetham
29. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Nasal Consonants
Group of sounds consisting of /m/,/n/,/η/
Produced with glottal Excitation and vocaltract totally
constricted along the oral passageway
Velam is lowered to block the air passage through oral
cavity and allowing through nasal cavity
Due the acoustic coupling of oral cavity to the pharynx, anti
resonances will be created
/m/,/n/ and /η/ are produced by the constiction at lips,
behind the teeth and at velum, respectively.
Govind CEN, Amrita Vishwa Vidyapeetham
31. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Unvoiced Fricatives
Produced by exciting vocaltract with a turbulant airflow
through a narrow constriction
/f/("four"),/θ/("thing"),/s/("sat") and /sh/ ("shut") are the
class of fricative sounds
/f/: Constriction at teeth
/s/: Constriction near middle of oral cavity
/sh/: constriction at the end of oral tract
Govind CEN, Amrita Vishwa Vidyapeetham
32. Organization: Speech Processing
Prerequisites
Introduction
Speech Production
Representation of Speech Signals
Spectro-Temporal Representation
classification of Phonemes
Voiced Fricatives
/v/("vat"),/δ/("zoo"),/z/("zoo") and /zh/("azure") are the class
of fricative sounds
/v/: Constriction at teeth
/z/: Constriction near middle of oral cavity
/zh/: constriction at the end of oral tract
Except glottal vibrations, the place of articulation remains
same as that of unvoiced fricatives
Govind CEN, Amrita Vishwa Vidyapeetham