Title: Robust Speech Recognition of Uncertain or Missing Data
Editors: Dorothea Kolossa and Reinhold Haeb-Umbach
Publisher: Springer
Year: 2011
ISBN 978-3-642-21316-8
Link:
http://www.springer.com/engineering/signals/book/978-3-642-21316-8?detailsPage=authorsAndEditors

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.