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 invarious 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.
