Download or read book Microphone Array Processing for Robust Speech Recognition written by Michael Seltzer and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Download or read book Studies on Microphone Array Processing and Time frequency Masking for Robust Automatic Speech Recognition written by Marco Kühne and published by . This book was released on 2009 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to deploy automatic speech recognition technology in real world scenarios it is necessary to handle cocktail-party-like environments with multiple speech and noise sources. Despite several decades of research the noise robustness of state-of-the-art speech recognizers still falls short in comparison with human capabilities. This dissertation focuses on the development of computational models for automatic speech separation and recognition inmulti-talker environments. Several aspects of cocktail-party processing are studied, ranging from source localization over source separation to speech recognition. The approach considered aims for a closer integration of microphone array processing and missing feature techniques for noise robust speech recognition. Front- and backend processing are linked together through the consistent application of time-frequency masking for both source separation and speech recognition. The use of cluster algorithms for automatically estimating these masks on the basis of spatial localization cues is investigated for anechoic and reverberant data. The incorporation of spatial observation weights and time-frequency context information is proposed as a means to increase the localization and segmentation performance of standard fuzzy cluster algorithms, particularly in echoic conditions. While the former helps to improve the localization accuracy by ignoring noisy observations the latter smoothes the fuzzy cluster membership levels by exploiting the high correlation of neighboring mask points. The resulting robust fuzzy cluster algorithm is integrated into a source separation system which combines the advantages of time-frequency masking with the separation capabilities of adaptive beamforming. The thesis also investigates a novel evidence model in the form of the bounded-Gauss-Uniform mixture probability density function for missing data speech recognition. In a number of simulated cocktail-party scenarios it is observed that this new evidence model offers superior recognition accuracy over existing evidence functions and a related binaural segregation model.
Download or read book Multiple Approaches to Robust Speech Recognition written by and published by . This book was released on 1992 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper compares several different approaches to robust speech recognition. We review CMU's ongoing research in the use of acoustical pre-processing to achieve robust speech recognition, and we present the results of the first evaluation of pre- processing in the context of the DARPA standard ATIS domain for spoken language systems. We also describe and compare the effectiveness of three complementary methods of signal processing for robust speech recognition: acoustical pre-processing, microphone array processing, and the use of physiologically- motivated models of peripheral signal processing. Recognition error rates are presented using these three approaches in isolation and in combination with each other for the speaker-independent continuous alphanumeric census speech recognition task.
Download or read book Microphone Arrays written by Michael Brandstein and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to provide a single complete reference on microphone arrays. Top researchers in this field contributed articles documenting the current state of the art in microphone array research, development and technological application.
Download or read book Microphone Array Signal Processing for Advancements in Robust Speech Systems written by Tao Yu and published by . This book was released on 2011 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech system performance degrades significantly in distant-talking environments, where the speech signals can be severely distorted by additive noise and reverberation. Microphone array processing techniques have presented a potential alternative to close-talking microphones by providing speech enhancement through spatial filtering and directional discrimination. Different from conventional array optimization criteria, such as Minimal Variance Distortionless Reponse, Maximal Signal-to-Noise Ratio or Minimal Mean Squared Error, this thesis presents a series of task-oriented and environment-oriented microphone array solutions for real world speech system applications. Primarily, four important tasks (e.g., blind beamforming, automatic speech recognition (ASR), speech quality enhancement and integrated voice activity detection (VAD) with speech enhancement) are considered in two typical acoustic environments (e.g., in-vehicle and conference room). Our objective is to optimize the microphone array front-end at a system level, directly advancing the performance of a given task for the whole speech system. Specifically, several new algorithms and systems are proposed in this thesis: variance of spectra flux based blind beamforming to identify target speech source in in-vehicle and conference room environments, order statistic filter based squared spectra enhancement for ASR in in-vehicle environment, integrated VAD and speech quality enhancement system in in-vehicle environment, fast relative transfer function identification for speech quality enhancement and ASR in conference room, position dependent spectra conversion for speech quality enhancement and ASR in in-vehicle and conference room, discriminative training based VAD for in-vehicle environment, and an efficient real-time microphone array based speech acquisition platform. Primary theoretical analysis and promising real/simulation evaluations on the proposed algorithms are also presented in this thesis.
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
Download or read book Acoustical and Environmental Robustness in Automatic Speech Recognition written by A. Acero and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained. The use of microphones other than a "close talking" headset also tends to severely degrade speech recognition -performance. Even in relatively quiet office environments, speech is degraded by additive noise from fans, slamming doors, and other conversations, as well as by the effects of unknown linear filtering arising reverberation from surface reflections in a room, or spectral shaping by microphones or the vocal tracts of individual speakers. Speech-recognition systems designed for long-distance telephone lines, or applications deployed in more adverse acoustical environments such as motor vehicles, factory floors, oroutdoors demand far greaterdegrees ofenvironmental robustness. There are several different ways of building acoustical robustness into speech recognition systems. Arrays of microphones can be used to develop a directionally-sensitive system that resists intelference from competing talkers and other noise sources that are spatially separated from the source of the desired speech signal.
Download or read book Robust Speech Recognition Using Microphone Arrays written by Iain A. McCowan and published by . This book was released on 2001 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Techniques for Noise Robustness in Automatic Speech Recognition written by Tuomas Virtanen and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field
Download or read book Robust Speech Recognition in a Car Using a Microphone Array written by Bowon Lee and published by . This book was released on 2006 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proposed approach has two steps: speech enhancement as a preprocessor of noisy speech signals, followed by the phoneme restoration for robust speech recognition against nonstationary noises given knowledge of H S and HN.
Download or read book Multi hypotheses Feedback for Robust Speech Recognition Using a Microphone Array Input written by Luca Giulio Brayda and published by . This book was released on 2007 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognizing speech in real environments is as much difficult as the amount of noise increases and the speaker is far from the microphone. Recent studies showed that speech quality in terms of signal to noise ratio (SNR) can be increased using microphone arrays. By exploiting the spatial correlation among multi-channel signals, one can steer the array toward the speaker (beamforming). This can be done by simply exploiting inter-channel destructive interference of noise with a delay-and-sum technique, where inter-sensor delays are estimated and applied to each channel signal. Alternatively, per-channel filters (filter-and-sum) can be implemented: these filters can be fixed or adapted on a per-channel or per-frame basis, depending on the chosen criterion. In this work we address the problem that increasing the SNR does not imply increasing recognition performance to the same extent. Seltzer (2004) proposes to apply an adaptive filter-and-sum beamformer based on a Maximum Likelihood criterion (Limabeam) rather than on the SNR. In this method, filters are adapted in an unsupervised way using clean speech models which best align noisy speech features. Then the recognizer uses the sum of the filtered signals to generate a final transcription. In this thesis we show that considering in parallel N-best hypotheses instead of the best one, prior to optimization, can increase recognition performance close to that of a supervised algorithm: in fact after the parallel optimizations the N-best list is automatically re-ranked and recognition errors can be recovered. The framework of the N-best Limabeam was tested when significant additive noise is present. Furthermore, the potential of delay-and-sum beamforming, of Limabeam and of the proposed framework was studied in a very reverberant meeting room, where the collected database mimic different talker positions and head orientations: the purpose is to estimate recognition-oriented filters or exploiting additional information related to the environment such as the room impulse responses.
Download or read book Robust Continuous Speech Recognition Using a Microphone Array written by D. Giuliani and published by . This book was released on 1995 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Microphone Arrays written by Jacob Benesty and published by Springer Nature. This book was released on 2023-08-09 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the motivation for using microphone arrays as opposed to using a single sensor for sound acquisition. The book then goes on to summarize the most useful ideas, concepts, results, and new algorithms therein. The material presented in this work includes analysis of the advantages of using microphone arrays, including dimensionality reduction to remove the redundancy while preserving the variability of the array signals using the principal component analysis (PCA). The authors also discuss benefits such as beamforming with low-rank approximations, fixed, adaptive, and robust distortionless beamforming, differential beamforming, and a new form of binaural beamforming that takes advantage of both beamforming and human binaural hearing properties to improve speech intelligibility. The book makes the microphone array signal processing theory and applications available in a complete and self-contained text. The authors attempt to explain the main ideas in a clear and rigorous way so that the reader can easily capture the potentials, opportunities, challenges, and limitations of microphone array signal processing. This book is written for those who work on the topics of microphone arrays, noise reduction, speech enhancement, speech communication, and human-machine speech interfaces.
Download or read book Microphone Array Processing for Speech Recognition in Single and Multi Speaker Environments written by Santosh Raghu Metla and published by . This book was released on 2005 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Microphone Array Processing for Speech Enhancement in Hearing Aids written by Michael W. Hoffman and published by . This book was released on 1992 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments written by Xiao-Lei Zhang and published by Elsevier. This book was released on 2024-09-04 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applications. The book begins by looking at the basics of deep learning and common deep network models, followed by front-end algorithms for deep learning-based speech denoising, speech detection, single-channel speech enhancement multi-channel speech enhancement, multi-speaker speech separation, and the applications of deep learning-based speech denoising in speaker verification and speech recognition. - Provides a comprehensive introduction to the development of deep learning-based robust speech processing - Covers speech detection, speech enhancement, dereverberation, multi-speaker speech separation, robust speaker verification, and robust speech recognition - Focuses on a historical overview and then covers methods that demonstrate outstanding performance in practical applications
