Download or read book Robust Acoustic Modeling and Front end Design for Distant Speech Recognition written by Seyedmahdad Mirsamadi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a significant increase in the popularity of voice-enabled technologies which use human speech as the primary interface with machines. Recent advancements in acoustic modeling and feature design have increased the accuracy of Automatic Speech Recognition (ASR) to levels that enable voice interfaces to be used in many applications. However, much of the current performance is dependent on the use of close-talking microphones, (i.e., scenarios in which the user speaks directly into a hand-held or body-worn microphone). There is still a rather large performance gap experienced in distant-talking scenarios in which speech is recorded by far-field microphones that are placed at a distance from the speaker. In such scenarios, the distorting effects of distance (such as room reverberation and environment noise) make the recognition task significantly more challenging. In this dissertation, we propose novel approaches for designing a distant-talking ASR front-end as well as training robust acoustic models to reduce the existing gap between far-field and close-talking ASR performance. Specifically, we i) propose a novel multi-channel front-end enhancement algorithm for improved ASR in reverberant rooms using distributed non-uniform microphone arrays with random unknown locations; ii) propose a novel neural network model training approach using adversarial training to improve the robustness of multi-condition acoustic models that are trained directly on far-field data; iii) study alternate neural network adaptation strategies for far-field adaptation to the acoustic properties of specific target environments. Experimental results are provided based on far-field benchmark tasks and datasets which demonstrate the effectiveness of the proposed approaches for increasing far-field robustness in ASR. Based on experiments using reverberated TIMIT sentences, the proposed multi-channel front-end provides WER improvements of +21.5% and +37.7% in two-channel and four-channel scenarios over a single-channel scenario in which the channel with best signal quality is selected. On the acoustic modeling side and based on results of experiments on AMI corpus, the proposed multi-domain training approach provides a relative character error rate reduction of +3.3% with respect to a conventional multi-condition trained baseline, and +25.4% with respect to a clean-trained baseline.
Download or read book Robust Speech Recognition in Embedded Systems and PC Applications written by Jean-Claude Junqua and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Speech Recognition in Embedded Systems and PC Applications provides a link between the technology and the application worlds. As speech recognition technology is now good enough for a number of applications and the core technology is well established around hidden Markov models many of the differences between systems found in the field are related to implementation variants. We distinguish between embedded systems and PC-based applications. Embedded applications are usually cost sensitive and require very simple and optimized methods to be viable. 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 while providing some perspectives, and goes over the complementary technologies that are necessary to build an application, such as dialog and user interface technologies. Robust Speech Recognition in Embedded Systems and PC Applications is divided into five chapters. The first one reviews the main difficulties encountered in automatic speech recognition when the type of communication is unknown. The second chapter focuses on environment-independent/adaptive speech recognition approaches and on the mainstream methods applicable to noise robust speech recognition. The third chapter discusses several critical technologies that contribute to making an application usable. It also provides some design recommendations on how to design prompts, generate user feedback and develop speech user interfaces. The fourth chapter reviews several techniques that are particularly useful for embedded systems or to decrease computational complexity. It also presents some case studies for embedded applications and PC-based systems. Finally, the fifth chapter provides a future outlook for robust speech recognition, emphasizing the areas that the author sees as the most promising for the future. Robust Speech Recognition in Embedded Systems and PC Applications serves as a valuable reference and although not intended as a formal University textbook, contains some material that can be used for a course at the graduate or undergraduate level. It is a good complement for the book entitled Robustness in Automatic Speech Recognition: Fundamentals and Applications co-authored by the same author.
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 Acoustic Modeling Methods for Robust Speech Recognition in Teleservice Conditions written by Robert S. van Kommer and published by . This book was released on 2005 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Distant Speech Recognition written by Matthias Woelfel and published by John Wiley & Sons. This book was released on 2009-04-20 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems Gives relevant background information in acoustics and filter techniques, Explains the extraction and enhancement of classification relevant speech features Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques Discusses the use of multi-microphone configurations for speaker tracking and channel combination Presents several applications of the methods and technologies described in this book Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.
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
Download or read book Effective Acoustic Modeling for Robust Speaker Recognition written by Taufiq Hasan Al Banna and published by . This book was released on 2013 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robustness due to mismatched train/test conditions is the biggest challenge facing the speaker recognition community today, with transmission channel and environmental noise degradation being the prominent factors. Performance of state-of-the art speaker recognition methods aim at mitigating these factors by effectively modeling speech in multiple recording conditions, so that it can learn to distinguish between inter-speaker and intra-speaker variability. The increasing demand and availability of large development corpora introduces difficulties in effective data utilization and computationally efficient modeling. Traditional compensation strategies operate on higher dimensional utterance features, known as supervectors, which are obtained from the acoustic modeling of short-time features. Feature compensation is performed during front-end processing. Motivated by the covariance structure of conventional acoustic features, we envision that feature normalization and compensation can be integrated into the acoustic modeling. In this dissertation, we investigate the following fundamental research challenges: (i) analysis of data requirements for effective and efficient background model training, (ii) introducing latent factor analysis modeling of acoustic features, (iii) integration of channel compensation strategies in mixture-models, and (iv) development of noise robust background models using factor analysis. The effectiveness of the proposed solutions are demonstrated in various noisy and channel degraded conditions using the recent evaluation datasets released by the National Institute of Standards and Technology (NIST). These research accomplishments make an important step towards improving speaker recognition robustness in diverse acoustic conditions.
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 Reverberation Modeling for Robust Distant talking Speech Recognition written by Armin Martin Sehr and published by . This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Discriminant Training of Front end and Acoustic Modeling Stages to Heterogeneous Acoustic Environments for Multi stream Automatic Speech Recognition written by Michael Lee Shire and published by . This book was released on 2000 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book High performance Automatic Speech Recognition Via Enhanced Front end Analysis and Acoustic Modeling written by Liang Gu and published by . This book was released on 2001 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Signal Processing written by Jacob Benesty and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the first time, a reference on the most relevant applications of adaptive filtering techniques. Top researchers in the field contributed chapters addressing applications in acoustics, speech, wireless and networking, where research is still very active and open.
Download or read book High Dimensional Representations for Robust Speech Recognition written by Jibran Khan Yousafzai and published by . This book was released on 2010 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies comparing automatic speech recognition (ASR) to human speech recognition (HSR) show that machines still lack the level of robustness inherent to the humans. Conventional speech recognizers use low-dimensional front-ends based on spectral representations that are derived from nonlinear compression of speech signals. This thesis proposes an alternative front-end based on the high-dimensional acoustic waveform representation of speech. The rationale behind the development of this front-end is that the absence of lossy compression and non-linear transformations in the high-dimensional acoustic waveform domain can facilitate a straightforward noise compensation of the acoustic features arid may provide a better separation of phonetic units in adverse conditions. Robustness of the proposed front-end in comparison with the conventional ASR front-ends is investigated using discriminative classification methods on a phoneme classification task. Furthermore, discriminative classifiers that are tuned to the physical properties of speech perception are developed. Results show that the conventional front-ends achieve excellent performance in low noise conditions, but suffer severe performance degradation already at moderate noise levels. On the other hand, classification in the acoustic waveform domain, although not as accurate in low noise, exhibits a more robust behavior in severe noise. Their combination then yields consistently better performance than individual classification in either representation domain. More importantly, it even outperforms the classification with standard front-end features under optimal matched conditions.
Download or read book Noise Robust Front end Processing for Automatic Speech Recognition written by Qifeng Zhu and published by . This book was released on 2001 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Synergy of Acoustic phonetics and Auditory Modeling Towards Robust Speech Recognition written by Om Dadaji Deshmukh and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Front end Processing for Speech Applications Under Acoustic Mismatch Conditions written by Seyed Omid Sadjadi and published by . This book was released on 2014 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the constant increase in the demand for speech technology, there is a growing need to design more effective algorithms that can improve the robustness of automatic speech systems. While research in the field of speaker and language identification (SID and LID) has recently seen significant advancements, performance in real-life environments still remains a major challenge because of the variety of acoustic mismatch scenarios that may occur between training and test phases due to background noise, room reverberation, communication channel, etc. In this dissertation, we take several major steps towards the development of effective front-end solutions for speech applications under acoustic mismatch conditions.
Download or read book Auditory Front Ends for Noise Robust Automatic Speech Recognition written by 葉佳璋 and published by . This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:
