Robust Speech Recognition of Uncertain or Missing Data - Theory and Applications (Paperback, 2011 ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Release date:	November 2014
First published:	2011
Editors:	Dorothea Kolossa • Reinhold Haeb-Umbach
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Paperback
Pages:	380
Edition:	2011 ed.
ISBN-13:	978-3-642-43868-4
Categories:	Books > Language & Literature > Language & linguistics > Computational linguistics Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	3-642-43868-7
Barcode:	9783642438684

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