Distant Speech Recognition
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Distant Speech Recognition
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Author : Matthias Woelfel
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-20
Distant Speech Recognition written by Matthias Woelfel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-20 with Technology & Engineering categories.
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.
A Study Of Adaptive Enhancement Methods For Improved Distant Speech Recognition
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Author : Andrew Richard Titus
language : en
Publisher:
Release Date : 2018
A Study Of Adaptive Enhancement Methods For Improved Distant Speech Recognition written by Andrew Richard Titus and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech.
Distant Speech Recognition Of Natural Spontaneous Multi Party Conversations
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Author : Yulan Liu
language : en
Publisher:
Release Date : 2017
Distant Speech Recognition Of Natural Spontaneous Multi Party Conversations written by Yulan Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Blind Speech Separation In Distant Speech Recognition Front End Processing
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Author : Rahil Mahdian Toroghi
language : en
Publisher:
Release Date : 2016
Blind Speech Separation In Distant Speech Recognition Front End Processing written by Rahil Mahdian Toroghi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Robust Acoustic Modeling And Front End Design For Distant Speech Recognition
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Author : Seyedmahdad Mirsamadi
language : en
Publisher:
Release Date : 2017
Robust Acoustic Modeling And Front End Design For Distant Speech Recognition written by Seyedmahdad Mirsamadi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Acoustical engineering categories.
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.
New Era For Robust Speech Recognition
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Author : Shinji Watanabe
language : en
Publisher: Springer
Release Date : 2017-10-30
New Era For Robust Speech Recognition written by Shinji Watanabe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-30 with Computers categories.
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.
Model Based Sparse Component Analysis For Multiparty Distant Speech Recognition
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Author : Afsaneh Asaei
language : en
Publisher:
Release Date : 2013
Model Based Sparse Component Analysis For Multiparty Distant Speech Recognition written by Afsaneh Asaei and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Subband Beamforming With Higher Order Statistics For Distant Speech Recognition
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Author : Kenichi Kumatani
language : en
Publisher:
Release Date : 2010
Subband Beamforming With Higher Order Statistics For Distant Speech Recognition written by Kenichi Kumatani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
2000 Ieee International Conference On Acoustics Speech And Signal Processing
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Author :
language : en
Publisher:
Release Date : 2000
2000 Ieee International Conference On Acoustics Speech And Signal Processing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Electro-acoustics categories.
Large Receptive Field Convolutional Neural Networks For Robust Speech Recognition
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Author : Salar Jafarlou
language : en
Publisher:
Release Date : 2020
Large Receptive Field Convolutional Neural Networks For Robust Speech Recognition written by Salar Jafarlou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Machine learning categories.
Despite significant efforts over the last few years to build a robust automatic speech recognition (ASR) systems for different acoustic settings, the performance of the current state-of-the-art technologies significantly degrades in noisy reverberant environments. Convolutional Neural Networks (CNNs) have been successfully used to achieve substantial improvements in many speech processing applications including distant speech recognition (DSR). However, standard CNN architectures were not efficient in capturing long-term speech dynamics, which are essential in the design of a robust DSR system. In this thesis, we address this issue by investigating variants of large receptive field CNNs (LRF-CNNs) which include deeply recursive networks, dilated convolutional neural networks, and stacked hourglass networks. To compare the efficacy of the aforementioned architectures with the standard CNN for Wall Street Journal (WSJ) corpus, we use a hybrid DNN-HMM based speech recognition system. Then in order to evaluate the system performances in reverberated environments (the case for distant speech recognition) we evaluated the system in both simulated and realistic reverberated environments. For the former, we used realistic room impulse responses (RIRs) to simulate the reverberated versions from a clean channel. Finally, for realistic reverberation settings, we used UTD-Distance corpus to evaluate our system. Our experiments show that with fixed number of parameters across all architectures, the large receptive field networks show consistent improvements over the standard CNNs for both clean and distant speech. Amongst the explored LRF-CNNs, stacked hourglass network has shown improvements with a 8.9% relative reduction in word error rate (WER) and 10.7 % relative improvement in frame accuracy compared to the standard CNNs for distant simulation setups. Stack of hourglass also gave a 13.68 % and 12.90 % relative reduction for 1 m and 3 m distanced microphones respectively. For 6 m far microphones recursive networks were the one with the most WER gain of 7.46 %. This thesis is a study on a set of unsupervised techniques achieved by modifications on acoustic modeling component of the HMM-based ASR engine for robustness in reverberate environments. These techniques showed a consistent improvements in both simulated and realistic settings and demonstrates a track of research in the field of alternative acoustic modeling structures.