rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Robust Speech Recognition of Uncertain or Missing Data
    sub title: Theory and Applications
  name:
    name part: Kolossa, Dorothea.
    role:
      role term: editor.
  name:
    name part: Häb-Umbach, Reinhold.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 380 p. online resource.
  abstract: 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. .
  table of contents: 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.
  note: edited by Dorothea Kolossa, Reinhold Häb-Umbach.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational linguistics
  subject:
    topic: Engineering
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computational Linguistics
  classification: TK5102.9
  classification: TA1637-1638
  classification: TK7882.S65
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783642213175
  identifier: http://dx.doi.org/10.1007/978-3-642-21317-5
  record info:
    record creation date: 110712
    record change date: 20150520195618.0
    record identifier: 978-3-642-21317-5