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Titlebook: New Era for Robust Speech Recognition; Exploiting Deep Lear Shinji Watanabe,Marc Delcroix,John R. Hershey Book 2017 Springer International

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書(shū)目名稱(chēng)New Era for Robust Speech Recognition
副標(biāo)題Exploiting Deep Lear
編輯Shinji Watanabe,Marc Delcroix,John R. Hershey
視頻videohttp://file.papertrans.cn/666/665184/665184.mp4
概述Field of automatic speech recognition has evolved greatly since the introduction of deep learning.Covers the state-of-the-art in noise robustness for deep neural-network-based speech recognition.Inclu
圖書(shū)封面Titlebook: New Era for Robust Speech Recognition; Exploiting Deep Lear Shinji Watanabe,Marc Delcroix,John R. Hershey Book 2017 Springer International
描述.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..
出版日期Book 2017
關(guān)鍵詞Speech Recognition; Speech Processing; Natural Language Processing (NLP); Automatic Speech Recognition
版次1
doihttps://doi.org/10.1007/978-3-319-64680-0
isbn_softcover978-3-319-87849-2
isbn_ebook978-3-319-64680-0
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Sequence-Discriminative Training of Neural Networksinator language model order and frame-smoothing, that may improve the recognition performance. We further propose a two-forward-pass procedure to speed up sequence-discriminative training when memory is the main constraint. Experiments were conducted on the AMI meeting corpus.
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Discriminative Beamforming with Phase-Aware Neural Networks for Speech Enhancement and Recognitionopy cost function of ASR. In our experiments, the BF network is trained with both artificially generated and real microphone array signals. On the AMI meeting transcription, we found that the trained BF network produces competitive ASR results compared to traditional delay-and-sum beamforming on unseen array signals.
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