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Development Of A Speech Enhancement Algorithm Which Improves Speech Quality And Intelligibility
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Book Synopsis Development of a Speech Enhancement Algorithm which Improves Speech Quality and Intelligibility by : Vahid Montazeri
Download or read book Development of a Speech Enhancement Algorithm which Improves Speech Quality and Intelligibility written by Vahid Montazeri and published by . This book was released on 2013 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current speech enhancement methods are able to attenuate the level of background noise. Remarkable amount of quality improvement has been reported in literature using these methods in the presence of background noise. However, research done in recent years show that these algorithms cannot improve speech intelligibility and in some cases they decreases the intelligibility rate, as compared with the noisy speech signal. This dissertation will address the reasons why current speech enhancement methods are not able to improve intelligibility rate. Also, a new method will be presented, using Minimum Mean Square Error (MMSE) estimator and pre- and post- signal processing filters. Moreover, an estimator will be proposed which is able to minimize speech distortion and can be used as an alternative for MMSE estimator. Based on the subjective and objective experiments, the proposed algorithms are able to improve speech quality and intelligibility in the presence of a variety of background noises and SNR levels from 0 to 10 dB.
Book Synopsis Speech Enhancement by : Philipos C. Loizou
Download or read book Speech Enhancement written by Philipos C. Loizou and published by CRC Press. This book was released on 2013-02-25 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the proliferation of mobile devices and hearing devices, including hearing aids and cochlear implants, there is a growing and pressing need to design algorithms that can improve speech intelligibility without sacrificing quality. Responding to this need, Speech Enhancement: Theory and Practice, Second Edition introduces readers to the basic pr
Book Synopsis Speech Enhancement by : Philipos C. Loizou
Download or read book Speech Enhancement written by Philipos C. Loizou and published by CRC Press. This book was released on 2007-06-07 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers traditional speech enhancement algorithms, such as spectral subtraction and Wiener filtering algorithms as well as state-of-the-art algorithms including minimum mean-squared error algorithms that incorporate signal-presence uncertainty and subspace algorithms that incorporate psychoacoustic models. The coverage includes objective and subjective measures used to evaluate speech quality and intelligibility. Divided into three parts, the book presents the digital-signal processing and speech signal fundamentals needed to understand speech enhancement algorithms, the various classes of speech enhancement algorithms proposed over the last two decades, and the methods and measures used to evaluate the performance of speech enhancement algorithms.
Book Synopsis The Speech Enhancement Advanced Development Model by : Mark R. Weiss
Download or read book The Speech Enhancement Advanced Development Model written by Mark R. Weiss and published by . This book was released on 1978 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes the design, principles of operation, and performance characteristics of an Advanced Development Model of a speech enhancement unit. This unit improves the quality and intelligibility of speech signals by the removal of frequently encountered interference or noise from received or recorded speech signals. A high speed digital array processor and various time and frequency domain algorithms permits the detection and attenuation of narrowband noise (such as tones, hums, whistles, etc.) and impulse noise (such as ignition pulses, static, etc.) with minimum degradation to the speech signals. The enhancement unit provides automatic tracking and attenuation of interferring signals in real time and with a maximum lag of .15 second. The heart of the speech enhancement unit is a powerful computer known as a macro-array processor, or MAP, that performs all of the measurement, analysis, and processing of the input signal. It is supported by a digital magnetic tape unit used to program the MAP and a minicomputer which reads the program into the MAP. Tests on the unit showed attenuation of 30 to 50 db on both narrowband and impulse noise. Operational tests performed by trained Air Force personnel showed the unit to be highly effective in providing improved intelligibility and listenability which significantly reduced listener fatigue. Provision has been made in the design and fabrication of the speech enhancement unit to implement a technique for attenuating wideband random noise. This technique known as INTEL is one of the few known methods of suppressing this commonly encountered noise without severely distorting co-existing speech.
Book Synopsis Methods of Optimizing Speech Enhancement for Hearing Applications by : Fangqi Liu
Download or read book Methods of Optimizing Speech Enhancement for Hearing Applications written by Fangqi Liu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Speech and Audio Processing for Coding, Enhancement and Recognition by : Tokunbo Ogunfunmi
Download or read book Speech and Audio Processing for Coding, Enhancement and Recognition written by Tokunbo Ogunfunmi and published by Springer. This book was released on 2014-10-14 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the basic principles underlying the generation, coding, transmission and enhancement of speech and audio signals, including advanced statistical and machine learning techniques for speech and speaker recognition with an overview of the key innovations in these areas. Key research undertaken in speech coding, speech enhancement, speech recognition, emotion recognition and speaker diarization are also presented, along with recent advances and new paradigms in these areas.
Book Synopsis Speech Enhancement, Modeling and Recognition by : Danel Jaso
Download or read book Speech Enhancement, Modeling and Recognition written by Danel Jaso and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Communication via speech is one of the essential functions of human beings. Humans possess varied ways to retrieve information from the outside world or to communicate with each other and the three most important sources of information are speech, images and written text. For many purposes, speech stands out as the most efficient and convenient one. Speech not only conveys linguistic contents, but also communicates other useful information like the mood of the speaker. When speaker and listener are near to each other in a quiet environment, communication is generally easy and accurate. However, at a distance or in a noisy background, the listeners ability to understand suffers. Speech enhancement aims to improve speech quality by using various algorithms. The objective of enhancement is improvement in intelligibility and/or overall perceptual quality of degraded speech signal using audio signal processing techniques. Enhancing of speech degraded by noise, or noise reduction, is the most important field of speech enhancement, and used for many applications such as mobile phones, VoIP, teleconferencing systems, speech recognition, and hearing aids. This book covers important fields in speech processing such as speech enhancement, noise cancellation, multi-resolution spectral analysis, voice conversion, speech recognition and emotion recognition from speech in addition to applications. This book will be of immense useful for advanced graduate students, researchers and practicing engineers employed in speech processing.
Book Synopsis Speech Enhancement with Adaptive Thresholding and Kalman Filtering by : Mengjiao Zhao
Download or read book Speech Enhancement with Adaptive Thresholding and Kalman Filtering written by Mengjiao Zhao and published by . This book was released on 2018 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech enhancement has been extensively studied for many years and various speech enhancement methods have been developed during the past decades. One of the objectives of speech enhancement is to provide high-quality speech communication in the presence of background noise and concurrent interference signals. In the process of speech communication, the clean speech sig- nal is inevitably corrupted by acoustic noise from the surrounding environment, transmission media, communication equipment, electrical noise, other speakers, and other sources of interference. These disturbances can significantly degrade the quality and intelligibility of the received speech signal. Therefore, it is of great interest to develop efficient speech enhancement techniques to recover the original speech from the noisy observation. In recent years, various techniques have been developed to tackle this problem, which can be classified into single channel and multi-channel enhancement approaches. Since single channel enhancement is easy to implement, it has been a significant field of research and various approaches have been developed. For example, spectral subtraction and Wiener filtering, are among the earliest single channel methods, which are based on estimation of the power spectrum of stationary noise. However, when the noise is non-stationary, or there exists music noise and ambient speech noise, the enhancement performance would degrade considerably. To overcome this disadvantage, this thesis focuses on single channel speech enhancement under adverse noise environment, especially the non-stationary noise environment. Recently, wavelet transform based methods have been widely used to reduce the undesired background noise. On the other hand, the Kalman filter (KF) methods offer competitive denoising results, especially in non-stationary environment. It has been used as a popular and powerful tool for speech enhancement during the past decades. In this regard, a single channel wavelet thresholding based Kalman filter (KF) algorithm is proposed for speech enhancement in this thesis. The wavelet packet (WP) transform is first applied to the noise corrupted speech on a frame-by-frame basis, which decomposes each frame into a number of subbands. A voice activity detector (VAD) is then designed to detect the voiced/unvoiced frames of the subband speech. Based on the VAD result, an adaptive thresholding scheme is applied to each subband speech followed by the WP based reconstruction to obtain the pre-enhanced speech. To achieve a further level of enhancement, an iterative Kalman filter (IKF) is used to process the pre-enhanced speech. The proposed adaptive thresholding iterative Kalman filtering (AT-IKF) method is evaluated and compared with some existing methods under various noise conditions in terms of segmental SNR and perceptual evaluation of speech quality (PESQ) as two well-known performance indexes. Firstly, we compare the proposed adaptive thresholding (AT) scheme with three other threshold- ing schemes: the non-linear universal thresholding (U-T), the non-linear wavelet packet transform thresholding (WPT-T) and the non-linear SURE thresholding (SURE-T). The experimental results show that the proposed AT scheme can significantly improve the segmental SNR and PESQ for all input SNRs compared with the other existing thresholding schemes. Secondly, extensive computer simulations are conducted to evaluate the proposed AT-IKF as opposed to the AT and the IKF as standalone speech enhancement methods. It is shown that the AT-IKF method still performs the best. Lastly, the proposed ATIKF method is compared with three representative and popular meth- ods: the improved spectral subtraction based speech enhancement algorithm (ISS), the improved Wiener filter based method (IWF) and the representative subband Kalman filter based algorithm (SIKF). Experimental results demonstrate the effectiveness of the proposed method as compared to some previous works both in terms of segmental SNR and PESQ.
Book Synopsis Speech Enhancement with Improved Deep Learning Methods by : Mojtaba Hasannezhad
Download or read book Speech Enhancement with Improved Deep Learning Methods written by Mojtaba Hasannezhad and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In real-world environments, speech signals are often corrupted by ambient noises during their acquisition, leading to degradation of quality and intelligibility of the speech for a listener. As one of the central topics in the speech processing area, speech enhancement aims to recover clean speech from such a noisy mixture. Many traditional speech enhancement methods designed based on statistical signal processing have been proposed and widely used in the past. However, the performance of these methods was limited and thus failed in sophisticated acoustic scenarios. Over the last decade, deep learning as a primary tool to develop data-driven information systems has led to revolutionary advances in speech enhancement. In this context, speech enhancement is treated as a supervised learning problem, which does not suffer from issues faced by traditional methods. This supervised learning problem has three main components: input features, learning machine, and training target. In this thesis, various deep learning architectures and methods are developed to deal with the current limitations of these three components. First, we propose a serial hybrid neural network model integrating a new low-complexity fully-convolutional convolutional neural network (CNN) and a long short-term memory (LSTM) network to estimate a phase-sensitive mask for speech enhancement. Instead of using traditional acoustic features as the input of the model, a CNN is employed to automatically extract sophisticated speech features that can maximize the performance of a model. Then, an LSTM network is chosen as the learning machine to model strong temporal dynamics of speech. The model is designed to take full advantage of the temporal dependencies and spectral correlations present in the input speech signal while keeping the model complexity low. Also, an attention technique is embedded to recalibrate the useful CNN-extracted features adaptively. Through extensive comparative experiments, we show that the proposed model significantly outperforms some known neural network-based speech enhancement methods in the presence of highly non-stationary noises, while it exhibits a relatively small number of model parameters compared to some commonly employed DNN-based methods. Most of the available approaches for speech enhancement using deep neural networks face a number of limitations: they do not exploit the information contained in the phase spectrum, while their high computational complexity and memory requirements make them unsuited for real-time applications. Hence, a new phase-aware composite deep neural network is proposed to address these challenges. Specifically, magnitude processing with spectral mask and phase reconstruction using phase derivative are proposed as key subtasks of the new network to simultaneously enhance the magnitude and phase spectra. Besides, the neural network is meticulously designed to take advantage of strong temporal and spectral dependencies of speech, while its components perform independently and in parallel to speed up the computation. The advantages of the proposed PACDNN model over some well-known DNN-based SE methods are demonstrated through extensive comparative experiments. Considering that some acoustic scenarios could be better handled using a number of low-complexity sub-DNNs, each specifically designed to perform a particular task, we propose another very low complexity and fully convolutional framework, performing speech enhancement in short-time modified discrete cosine transform (STMDCT) domain. This framework is made up of two main stages: classification and mapping. In the former stage, a CNN-based network is proposed to classify the input speech based on its utterance-level attributes, i.e., signal-to-noise ratio and gender. In the latter stage, four well-trained CNNs specialized for different specific and simple tasks transform the STMDCT of noisy input speech to the clean one. Since this framework is designed to perform in the STMDCT domain, there is no need to deal with the phase information, i.e., no phase-related computation is required. Moreover, the training target length is only one-half of those in the previous chapters, leading to lower computational complexity and less demand for the mapping CNNs. Although there are multiple branches in the model, only one of the expert CNNs is active for each time, i.e., the computational burden is related only to a single branch at anytime. Also, the mapping CNNs are fully convolutional, and their computations are performed in parallel, thus reducing the computational time. Moreover, this proposed framework reduces the latency by %55 compared to the models in the previous chapters. Through extensive experimental studies, it is shown that the MBSE framework not only gives a superior speech enhancement performance but also has a lower complexity compared to some existing deep learning-based methods.
Book Synopsis Robust Automatic Speech Recognition by : Jinyu Li
Download or read book Robust Automatic Speech Recognition written by Jinyu Li and published by Academic Press. This book was released on 2015-10-30 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition Learn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology development Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years
Book Synopsis Speech Enhancement by : Jacob Benesty
Download or read book Speech Enhancement written by Jacob Benesty and published by Elsevier. This book was released on 2014-01-04 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains. - First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement - Bridges the gap between optimal filtering methods and subspace approaches - Includes original presentation of subspace methods from different perspectives
Book Synopsis Speech Enhancement by : Shoji Makino
Download or read book Speech Enhancement written by Shoji Makino and published by Springer Science & Business Media. This book was released on 2005-03-17 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field.
Book Synopsis Single-Channel Speech Enhancement Based on Deep Neural Networks by : Zhiheng Ouyang
Download or read book Single-Channel Speech Enhancement Based on Deep Neural Networks written by Zhiheng Ouyang and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech enhancement (SE) aims to improve the speech quality of the degraded speech. Recently, researchers have resorted to deep-learning as a primary tool for speech enhancement, which often features deterministic models adopting supervised training. Typically, a neural network is trained as a mapping function to convert some features of noisy speech to certain targets that can be used to reconstruct clean speech. These methods of speech enhancement using neural networks have been focused on the estimation of spectral magnitude of clean speech considering that estimating spectral phase with neural networks is difficult due to the wrapping effect. As an alternative, complex spectrum estimation implicitly resolves the phase estimation problem and has been proven to outperform spectral magnitude estimation. In the first contribution of this thesis, a fully convolutional neural network (FCN) is proposed for complex spectrogram estimation. Stacked frequency-dilated convolution is employed to obtain an exponential growth of the receptive field in frequency domain. The proposed network also features an efficient implementation that requires much fewer parameters as compared with conventional deep neural network (DNN) and convolutional neural network (CNN) while still yielding a comparable performance. Consider that speech enhancement is only useful in noisy conditions, yet conventional SE methods often do not adapt to different noisy conditions. In the second contribution, we proposed a model that provides an automatic "on/off" switch for speech enhancement. It is capable of scaling its computational complexity under different signal-to-noise ratio (SNR) levels by detecting clean or near-clean speech which requires no processing. By adopting information maximizing generative adversarial network (InfoGAN) in a deterministic, supervised manner, we incorporate the functionality of SNR-indicator into the model that adds little additional cost to the system. We evaluate the proposed SE methods with two objectives: speech intelligibility and application to automatic speech recognition (ASR). Experimental results have shown that the CNN-based model is applicable for both objectives while the InfoGAN-based model is more useful in terms of speech intelligibility. The experiments also show that SE for ASR may be more challenging than improving the speech intelligibility, where a series of factors, including training dataset and neural network models, would impact the ASR performance.
Book Synopsis Speech Enhancement in Real-world Environments Using State-space Based Algorithms by : Frédéric Mustière
Download or read book Speech Enhancement in Real-world Environments Using State-space Based Algorithms written by Frédéric Mustière and published by . This book was released on 2010 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Speech Dereverberation by : Patrick A. Naylor
Download or read book Speech Dereverberation written by Patrick A. Naylor and published by Springer Science & Business Media. This book was released on 2010-07-27 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech Dereverberation gathers together an overview, a mathematical formulation of the problem and the state-of-the-art solutions for dereverberation. Speech Dereverberation presents current approaches to the problem of reverberation. It provides a review of topics in room acoustics and also describes performance measures for dereverberation. The algorithms are then explained with mathematical analysis and examples that enable the reader to see the strengths and weaknesses of the various techniques, as well as giving an understanding of the questions still to be addressed. Techniques rooted in speech enhancement are included, in addition to a treatment of multichannel blind acoustic system identification and inversion. The TRINICON framework is shown in the context of dereverberation to be a generalization of the signal processing for a range of analysis and enhancement techniques. Speech Dereverberation is suitable for students at masters and doctoral level, as well as established researchers.
Book Synopsis Speech Separation by Humans and Machines by : Pierre Divenyi
Download or read book Speech Separation by Humans and Machines written by Pierre Divenyi and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advances in Nonlinear Speech Processing by : Carlos M. Travieso-González
Download or read book Advances in Nonlinear Speech Processing written by Carlos M. Travieso-González and published by Springer. This book was released on 2011-11-03 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 5th International Conference on Nonlinear Speech Processing, NoLISP 2011, held in Las Palmas de Gran Canaria, Spain, in November 2011. The purpose of the workshop is to present and discuss new ideas, techniques and results related to alternative approaches in speech processing that may depart from the main stream. The 33 papers presented together with 2 keynote talks were carefully reviewed and selected for inclusion in this book. The topics of NOLISP 2011 were non-linear approximation and estimation; non-linear oscillators and predictors; higher-order statistics; independent component analysis; nearest neighbors; neural networks; decision trees; non-parametric models; dynamics of non-linear systems; fractal methods; chaos modeling; and non-linear differential equations.
