Robust speech recognition of uncertain or missing data : theory and applications
著者
書誌事項
Robust speech recognition of uncertain or missing data : theory and applications
Springer, c2011
大学図書館所蔵 全4件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
内容説明・目次
内容説明
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.
目次
Chap. 1 - Introduction.-
Part I - Theoretical Foundations.-
Chap. 2 - Uncertainty Decoding and Conditional Bayesian Estimation.- Chap. 3 - Uncertainty Propagation.-
Part II - Applications.-
Chap. 4 - Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition.- Chap. 5 - Model-Based Approaches to Handling Uncertainty.- Chap. 6 - Reconstructing Noise-Corrupted Spectrographic Components for Robust Speech Recognition.- Chap. 7 - Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data.- Chap. 8 - Conditional Bayesian Estimation Employing a Phase-Sensitive Estimation Model for Noise-Robust Speech Recognition.-
Part III - Reverberation Robustness.-
Chap. 9 - Variance Compensation for Recognition of Reverberant Speech with Dereverberation Processing.- Chap. 10 - A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition.-
Part IV - Applications: Multiple Speakers and Modalities.-
Chap. 11 - Evidence Modelling for Missing Data Speech Recognition Using Small Microphone Arrays.- Chap. 12 - Recognition of Multiple Speech Sources Using ICA.- Chap. 13 - Use of Missing and Unreliable Data for Audiovisual Speech Recognition.-
Index.
「Nielsen BookData」 より