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 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

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.

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

"The performance of an automatic speech recognition (ASR) system degrades dramatically when speech is corrupted by background noise. In many ASR applications, however, the presence of the background noise is unavoidable. Feature representations in ASR are usually derived from the short-time spectral magnitude of the speech signal, known as the speech spectrogram. The goal of the work in this thesis is to develop noise robust ASR systems by reconstructing noise corrupted speech spectrograms. This is addressed as a data imputation problem within the framework of missing feature theory in computational auditory scene analysis. This thesis presents a number of data imputation techniques which can add noise robustness to an ASR system while making minimum assumptions about the characteristics of the background noise. There are three major contributions in this thesis work. The first relates to the spectrographic mask estimation which is performed to identify noise corrupted features. Having identified the noise corrupted speech features, a spectrogram reconstruction technique is employed to estimate the underlying clean features and reconstruct the noise corrupted features. A mask estimation method, based on speech enhancement techniques presented previously in the literature, is incorporated in a spectrogram reconstruction approach for noise robust ASR. The presented mask estimation technique is shown to perform well both in stationary and non-stationary noisy environments. More importantly, this technique does not require any prior knowledge of the background noise type or the SNR level.The second contribution of this thesis is a filterbank based approach to spectrogram reconstruction based on discrete wavelet transform (DWT) de-noising. In these techniques, speech spectrogram coefficients are input to a DWT filterbank. Most of the spectrogram reconstruction approaches presented in the literature are model-based techniques that can only provide accurate estimates of the underlying clean speech when the characteristics of the noise corrupted features do not deviate from those of the model. Discrete wavelet transform (DWT) based de-noising methods have been used for signal reconstruction, but often require that the background noise is stationary and modeled by a Gaussian distribution. A novel approach is presented in this thesis for incorporating the information derived from spectrographic masks in a DWT-based de-noising method. It will be shown that the proposed approach reduces the impact of model mismatch associated with parametric approaches and exploits the robustness of non-parametric wavelet de-noising approach. This technique, however, can perform at its best only if some parameters are tuned to the noise conditions. The third contribution of this thesis is a procedure which combines multiple DWT-based reconstructed spectral features using a closed loop optimization algorithm which is related to the overall performance of the ASR system. The feature channels are formed from an ensemble of reconstructed spectrograms generated by applyingDWT-based spectrogram reconstruction with multiple parameter settings. The spectrograms associated with these feature channels differ in the degreeto which spectral information is suppressed across multiple scales and frequencybands.A consistent increase in word accuracy is reported for this multi-channelperformance monitoring approach with respect to animplementation of a more well known minimum mean squared error approach formissing feature based spectrogram reconstruction. " --

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.