Speech Separation By Humans And Machines
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Speech Separation By Humans And Machines
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Author : Pierre Divenyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-01-16
Speech Separation By Humans And Machines written by Pierre Divenyi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-16 with Technology & Engineering categories.
This book is appropriate for those specializing in speech science, hearing science, neuroscience, or computer science and engineers working on applications such as automatic speech recognition, cochlear implants, hands-free telephones, sound recording, multimedia indexing and retrieval.
Voice Communication Between Humans And Machines
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Author : for the National Academy of Sciences
language : en
Publisher: National Academies Press
Release Date : 1994-02-01
Voice Communication Between Humans And Machines written by for the National Academy of Sciences and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-02-01 with Technology & Engineering categories.
Science fiction has long been populated with conversational computers and robots. Now, speech synthesis and recognition have matured to where a wide range of real-world applicationsâ€"from serving people with disabilities to boosting the nation's competitivenessâ€"are within our grasp. Voice Communication Between Humans and Machines takes the first interdisciplinary look at what we know about voice processing, where our technologies stand, and what the future may hold for this fascinating field. The volume integrates theoretical, technical, and practical views from world-class experts at leading research centers around the world, reporting on the scientific bases behind human-machine voice communication, the state of the art in computerization, and progress in user friendliness. It offers an up-to-date treatment of technological progress in key areas: speech synthesis, speech recognition, and natural language understanding. The book also explores the emergence of the voice processing industry and specific opportunities in telecommunications and other businesses, in military and government operations, and in assistance for the disabled. It outlines, as well, practical issues and research questions that must be resolved if machines are to become fellow problem-solvers along with humans. Voice Communication Between Humans and Machines provides a comprehensive understanding of the field of voice processing for engineers, researchers, and business executives, as well as speech and hearing specialists, advocates for people with disabilities, faculty and students, and interested individuals.
Blind Speech Separation
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Author : Shoji Makino
language : en
Publisher: Springer
Release Date : 2010-11-30
Blind Speech Separation written by Shoji Makino and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-30 with Technology & Engineering categories.
This is the world’s first edited book on independent component analysis (ICA)-based blind source separation (BSS) of convolutive mixtures of speech. This book brings together a small number of leading researchers to provide tutorial-like and in-depth treatment on major ICA-based BSS topics, with the objective of becoming the definitive source for current, comprehensive, authoritative, and yet accessible treatment.
Speechreading By Humans And Machines
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Author : David G. Stork
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Speechreading By Humans And Machines written by David G. Stork and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Technology & Engineering categories.
This book is one outcome of the NATO Advanced Studies Institute (ASI) Workshop, "Speechreading by Man and Machine," held at the Chateau de Bonas, Castera-Verduzan (near Auch, France) from August 28 to Septem ber 8, 1995 - the first interdisciplinary meeting devoted the subject of speechreading ("lipreading"). The forty-five attendees from twelve countries covered the gamut of speechreading research, from brain scans of humans processing bi-modal stimuli, to psychophysical experiments and illusions, to statistics of comprehension by the normal and deaf communities, to models of human perception, to computer vision and learning algorithms and hardware for automated speechreading machines. The first week focussed on speechreading by humans, the second week by machines, a general organization that is preserved in this volume. After the in evitable difficulties in clarifying language and terminology across disciplines as diverse as human neurophysiology, audiology, psychology, electrical en gineering, mathematics, and computer science, the participants engaged in lively discussion and debate. We think it is fair to say that there was an atmosphere of excitement and optimism for a field that is both fascinating and potentially lucrative. Of the many general results that can be taken from the workshop, two of the key ones are these: • The ways in which humans employ visual image for speech recogni tion are manifold and complex, and depend upon the talker-perceiver pair, severity and age of onset of any hearing loss, whether the topic of conversation is known or unknown, the level of noise, and so forth.
Implementation And Evaluation Of Gated Recurrent Unit For Speech Separation And Speech Enhancement
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Author : Sagar Shah
language : en
Publisher:
Release Date : 2019
Implementation And Evaluation Of Gated Recurrent Unit For Speech Separation And Speech Enhancement written by Sagar Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Biomedical engineering categories.
Hearing aids, automatic speech recognition (ASR) and many other communication systems work well when there is just one sound source with almost no echo, but their performance degrades in situations where more speakers are talking simultaneously or the reverberation is high. Speech separation and speech enhancement are core problems in the field of audio signal processing. Humans are remarkably capable of focusing their auditory attention on a single sound source within a noisy environment, by de-emphasizing all other voices and interferences in surroundings. This capability comes naturally to us humans. However, speech separation remains a significant challenge for computers. It is challenging for the following reasons: the wide variety of sound type, different mixing environment, and the unclear procedure to distinguish sources, especially for similar sounds. Also, perceiving speech in low signal/noise (SNR) conditions is hard for hearing-impaired listeners. Therefore, the motivation is to advance the speech separation algorithms to improve the intelligibility of noisy speech. Latest technologies aim to empower machines with similar abilities. Recently, the deep neural network methods achieved impressive successes in various problems, including speech enhancement, which the task to separate the clean speech of the noise mixture. Due to the advances in deep learning, speech separation can be viewed as a classification problem and treated as a supervised learning problem. Three main components of speech separation or speech enhancement using deep learning methods are acoustic features, learning machines, and training targets. This work aims to implement a single-channel speech separation and enhancement algorithm utilizing machine learning, deep neural networks (DNNs). An extensive set of speech from different speakers and noise data is collected to train a neural network model that predicts time-frequency masks from noisy and mixture speech signals. The algorithm is tested using various noises and combinations of different speakers. Its performance is evaluated in terms of speech quality and intelligibility. In this thesis, I am proposing a variant of the recurrent neural network, which is GRU (gated recurrent unit) for the speech separation and speech enhancement task. It is a simpler model than the LSTM (long short-term memory), which is used now for the task of speech enhancement and speech separation, consisting of a smaller number of parameters and matching the performance of the speech separation and speech enhancement of LSTM networks.
Speech Processing In Modern Communication
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Author : Israel Cohen
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-12-18
Speech Processing In Modern Communication written by Israel Cohen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-18 with Technology & Engineering categories.
Modern communication devices, such as mobile phones, teleconferencing systems, VoIP, etc., are often used in noisy and reverberant environments. Therefore, signals picked up by the microphones from telecommunication devices contain not only the desired near-end speech signal, but also interferences such as the background noise, far-end echoes produced by the loudspeaker, and reverberations of the desired source. These interferences degrade the fidelity and intelligibility of the near-end speech in human-to-human telecommunications and decrease the performance of human-to-machine interfaces (i.e., automatic speech recognition systems). The proposed book deals with the fundamental challenges of speech processing in modern communication, including speech enhancement, interference suppression, acoustic echo cancellation, relative transfer function identification, source localization, dereverberation, and beamforming in reverberant environments. Enhancement of speech signals is necessary whenever the source signal is corrupted by noise. In highly non-stationary noise environments, noise transients, and interferences may be extremely annoying. Acoustic echo cancellation is used to eliminate the acoustic coupling between the loudspeaker and the microphone of a communication device. Identification of the relative transfer function between sensors in response to a desired speech signal enables to derive a reference noise signal for suppressing directional or coherent noise sources. Source localization, dereverberation, and beamforming in reverberant environments further enable to increase the intelligibility of the near-end speech signal.
Implicit And Explicit Phase Modeling In Deep Learning Based Source Separation
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Author : Manuel Pariente
language : en
Publisher:
Release Date : 2021
Implicit And Explicit Phase Modeling In Deep Learning Based Source Separation written by Manuel Pariente and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Whether processed by humans or machines, speech occupies a central part of our daily lives, yet distortions such as noise or competing speakers reduce both human understanding and machine performance. Audio source separation and speech enhancement aim at solving this problem. To perform separation and enhancement, most traditional approaches rely on the magnitude short-time Fourier transform (STFT), thus discarding the phase. Thanks to their increased representational power, deep neural networks (DNNs) have recently made it possible to break that assumption and exploit the fine-grained spectro-temporal information provided by the phase. In this thesis, we study the impact of implicit and explicit phase modeling in deep discriminative and generative models with application to source separation and speech enhancement.In a first stage, we consider the task of discriminative source separation based on the encoder-masker-decoder framework popularized by TasNet. We propose a unified view of learned and fixed filterbanks and extend on two previously proposed learnable filterbanks by making them analytical, thus enabling the computation of the magnitude and phase of the resulting representation. We study the amount of information provided by the magnitude and phase components as a function of the window size. Results on the WHAM dataset show that for all filterbanks the best performance is achieved for short 2 ms windows and that, for such short windows, phase modeling is indeed crucial. Interestingly, this also holds for STFT-based models that even surpass the performance of oracle magnitude masking. This work has formed the basis of Asteroid, the PyTorch-based audio source separation toolkit for researchers, of which we then present the main features as well as example results obtained with it. Second, we tackle the speech enhancement task with an approach based on a popular deep generative model, the variational autoencoder (VAE), which models the complex STFT coefficients in a given time frame as independent zero-mean complex Gaussian variables whose variances depend on a latent representation. By combining a VAE model for the speech variances and a nonnegative matrix factorization (NMF) model for the noise variances, we propose a variational inference algorithm to iteratively infer these variances and derive an estimate of the clean speech signal. In particular, the encoder of the pretrained VAE can be used to estimate the variational approximation of the true posterior distribution, using the very same assumption made to train VAEs. Experiments show that the proposed method produces results on par with other VAE-based methods, while decreasing the computational cost by a factor of 36.Following on the above study, we integrate time-frequency dependency and phase modeling capabilities into the above VAE-based generative model by relaxing the time-frequency independence assumption and assuming a multivariate zero-mean Gaussian model over the entire complex STFT conditional to the latent representation. The covariance matrix of that model is parameterized by its sparse Cholesky factor which constitutes the VAE's output. The sparsity pattern is chosen so that local time and frequency dependencies can be expressed. We evaluate the proposed method for speech separation on the WSJ0 dataset as a function of the chosen dependency pattern.
Speech Communication
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Author : Douglas O'Shaughnessy
language : en
Publisher: Reading, Mass. : Addison-Wesley Publishing Company
Release Date : 1987
Speech Communication written by Douglas O'Shaughnessy and has been published by Reading, Mass. : Addison-Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Computers categories.
Speech And Audio Signal Processing
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Author : Ben Gold
language : en
Publisher: John Wiley & Sons
Release Date : 2011-08-23
Speech And Audio Signal Processing written by Ben Gold and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-23 with Technology & Engineering categories.
When Speech and Audio Signal Processing published in 1999, it stood out from its competition in its breadth of coverage and its accessible, intutiont-based style. This book was aimed at individual students and engineers excited about the broad span of audio processing and curious to understand the available techniques. Since then, with the advent of the iPod in 2001, the field of digital audio and music has exploded, leading to a much greater interest in the technical aspects of audio processing. This Second Edition will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution (most significantly the MP3) and a range of exciting new research areas in automatic music content processing (such as automatic transcription, music similarity, etc.) that have emerged in the past five years, driven by the digital music revolution. New chapter topics include: Psychoacoustic Audio Coding, describing MP3 and related audio coding schemes based on psychoacoustic masking of quantization noise Music Transcription, including automatically deriving notes, beats, and chords from music signals. Music Information Retrieval, primarily focusing on audio-based genre classification, artist/style identification, and similarity estimation. Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception-inspired techniques usually referred to as Computational Auditory Scene Analysis (CASA).
Speech And Human Machine Dialog
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Author : Wolfgang Minker
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18
Speech And Human Machine Dialog written by Wolfgang Minker and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-18 with Technology & Engineering categories.
Speech and Human-Machine Dialog focuses on the dialog management component of a spoken language dialog system. Spoken language dialog systems provide a natural interface between humans and computers. These systems are of special interest for interactive applications, and they integrate several technologies including speech recognition, natural language understanding, dialog management and speech synthesis. Due to the conjunction of several factors throughout the past few years, humans are significantly changing their behavior vis-à-vis machines. In particular, the use of speech technologies will become normal in the professional domain, and in everyday life. The performance of speech recognition components has also significantly improved. This book includes various examples that illustrate the different functionalities of the dialog model in a representative application for train travel information retrieval (train time tables, prices and ticket reservation). Speech and Human-Machine Dialog is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.