Environment Normalization for Robust Speech Recognition using Direct Cepstral Comparisons

In this paper we describe and evaluate a series of new algorithms

that compensate for the effects of unknown acoustical environments

or changes in environment. The algorithms use compensation

vectors that are added to the cepstral representations of

speech that is input to a speech recognition system. While these

vectors are computed from direct frame-by-frame comparisons of

cepstra of speech simultaneously recorded in the training environment

and various prototype testing environments, the compensation

algorithms do not assume that the acoustical characteristics of

the actual testing environment are known. The speciEc compensation

vector applied in a given frame depends on either physical

attributes such as SNR or presumed phonetic identity. The compensation

algorithms are evaluated using the 1992 ARPA 5000-

word WSJKSR corpus. The best system combines phonemebased

and SNR-based cepstral compensation with cepstral mean

normalization, and provides a 66.8% reduction in error rate over

baseline processing when tested using a standard suite of

unknown microphones.

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In  Proc. of the International Conference on Acoustics, Speech, and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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