Robust Speech Features for Speech Recognition in Hostile Environments
Robust Speech Features for Speech Recognition in Hostile Environments

Author: Aik Ming Toh

Publisher:

Published: 2007

Total Pages: 173

ISBN-13:

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Speech recognition systems have improved in robustness in recent years with respect to both speaker and acoustical variability. Nevertheless, it is still a challenge to deploy speech recognition systems in real-world applications that are exposed to diverse and significant level of noise. Robustness and recognition accuracy are the essential criteria in determining the extent of a speech recognition system deployed in real-world applications. This work involves development of techniques and extensions to extract robust features from speech and achieve substantial performance in speech recognition. Robustness and recognition accuracy are the top concern in this research. In this work, the robustness issue is approached using the front-end processing, in particular robust feature extraction. The author proposes an unified framework for robust feature and presents a comprehensive evaluation on robustness in speech features. The framework addresses three distinct approaches: robust feature extraction, temporal information inclusion and normalization strategies. The author discusses the issue of robust feature selection primarily in the spectral and cepstral context. Several enhancement and extensions are explored for the purpose of robustness. This includes a computationally efficient approach proposed for moment normalization. In addition, a simple back-end approach is incorporated to improve recognition performance in reverberant environments. Speech features in this work are evaluated in three distinct environments that occur in real-world scenarios. The thesis also discusses the effect of noise on speech features and their parameters. The author has established that statistical properties play an important role in mismatches. The significance of the research is strengthened by the evaluation of robust approaches in more than one scenario and the comparison with the performance of the state-of-the-art features. The contributions and limitations of each robust feature in all three different environments are highlighted. The novelty of the work lies in the diverse hostile environments which speech features are evaluated for robustness. The author has obtained recognition accuracy of more than 98.5% for channel distortion. Recognition accuracy greater than 90.0% has also been maintained for reverberation time 0.4s and additive babble noise at SNR 10dB. The thesis delivers a comprehensive research on robust speech features for speech recognition in hostile environments supported by significant experimental results. Several observations, recommendations and relevant issues associated with robust speech features are presented.

Robust Speech Recognition of Uncertain or Missing Data
Robust Speech Recognition of Uncertain or Missing Data

Author: Dorothea Kolossa

Publisher: Springer Science & Business Media

Published: 2011-07-14

Total Pages: 387

ISBN-13: 3642213170

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Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition. The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.

Robust Speech
Robust Speech

Author: Michael Grimm

Publisher: BoD – Books on Demand

Published: 2007-06-01

Total Pages: 471

ISBN-13: 3902613084

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This book on Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. The next chapters give several extensions to state-of-the-art HMM methods. Furthermore, a number of chapters particularly address the task of robust ASR under noisy conditions. Two chapters on the automatic recognition of a speaker's emotional state highlight the importance of natural speech understanding and interpretation in voice-driven systems. The last chapters of the book address the application of conversational systems on robots, as well as the autonomous acquisition of vocalization skills.

New Era for Robust Speech Recognition
New Era for Robust Speech Recognition

Author: Shinji Watanabe

Publisher: Springer

Published: 2017-10-30

Total Pages: 433

ISBN-13: 331964680X

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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.

Recent Advances in Robust Speech Recognition Technology
Recent Advances in Robust Speech Recognition Technology

Author: Javier Ramirez

Publisher: Bentham Science

Published: 2011

Total Pages: 223

ISBN-13: 1608051722

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"This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or whe"

Robust Speech Recognition of Uncertain or Missing Data
Robust Speech Recognition of Uncertain or Missing Data

Author: Dorothea Kolossa

Publisher: Springer

Published: 2013-01-02

Total Pages: 380

ISBN-13: 9783642213182

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Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition. The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.

Robust Speech Recognition in Adverse Environments
Robust Speech Recognition in Adverse Environments

Author: Brendon Troy Lilly

Publisher:

Published: 2000

Total Pages: 292

ISBN-13:

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Abstract: The performance of an automatic speech recognition system degrades drastically when there is a mismatch between training and testing environments. The aim of robust speech recognition is to overcome this mismatch. Numerous methods have been reported in the literature that attempt to provide robustness to this mismatch. This thesis investigates several different techniques at different stages of the recognition process that are suitable for robust speech recognition. All experiments are conducted on the ISOLET database. The TIMIT database was also used to confirm some of the experimental results.--A number of speech enhancement techniques have been used in the past for speech recognition to achieve robustness with respect to noise. A speech enhancement system attempts to reduce noise from the noisy speech signal and is used as a pre-processor to a speech recogniser. In this thesis, a singular value decomposition (SVD) based speech enhancement method is used for robust speech recognition. The speech recognition performance of the SVD method is compared to that of the popular spectral subtraction method.--Speech recognition performance is directly affected by the performance of the feature extraction stage. This thesis provides a comprehensive evaluation of a number of acoustic front-ends for robust speech recognition. It also investigates the use of human auditory properties for robust feature extraction. Two acoustic front-ends based on simultaneous masking and variable frequency and temporal resolutions are proposed and their performance is investigated for speech distorted by additive noise and channel distortion.--This thesis also investigates the degradation in speech recognition performance due to speech coding distortion. For this, seven different speech coders operating at different bit rates are simulated and the speech recogniser is utilised through each of these coders. The MAP adaptation technique is then applied to adapt the model parameters to the speech coding environment. The resulting system is found to perform well in the presence of the speech coding distortion.

Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition

Author: A. Acero

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 197

ISBN-13: 1461531225

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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.

Robust Speech Recognition in Embedded Systems and PC Applications
Robust Speech Recognition in Embedded Systems and PC Applications

Author: Jean-Claude Junqua

Publisher: Springer Science & Business Media

Published: 2000-05-31

Total Pages: 204

ISBN-13: 0792378733

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"Robust Speech Recognition in Embedded Systems and PC Applications reviews the problems of robust speech recognition, summarizes the current state of the art of robust speech recognition, provides perspective on the state of the art, and reviews the complementary technologies (i.e., dialog and user interface) necessary to build an application.".