This book focuses on automatic speech recognition in clean and noisy or reverbrant environments. Therefore, a parallel speech recognition system using TempoRAL Patterns (TRAPs) is described. The TRAPs are computed over a rather long temporal context for each critical band in the signals spectrum. Then, the features of the different bands are combined. Thus recognition only in certain bands is possible. This is beneficial if noise only occurs in parts of the spectrum. In this manner multiple speech recognizers are trained which analyze disjoint parts of the frequency domain. Each of the speech...

This book focuses on the adaptation of speech recognizers to noisy or reverberant environment. Therefore, three corpora in different noise and reverberation levels are presented. Speech recognition is used. Basics are omitted. As features Mel Frequency Cepstrum Coefficients (MFCC) and several variants of the TempoRAl Patterns (TRAPs) are employed. In order to improve speech recognition even further the following speech recognizer adaptation techniques are explored: Methods like maximum a posteriori (MAP), maximum likelihood linear regression (MLLR), and constrained MLLR (CMLLR) are described...