Abstract

This paper describes a series of cepstral-based compensation procedures that render the SPHINX-II continuous speech recognition system more robust with respect to acoustical changes in the environment. The first two algorithms, SNR based MultivaRiate gAussian based cepsTral normaliZation (SNR-based RATZ) and STAtistical Reestimation of HMMs (STAR), compensate for environmental degradation based on comparisons of simultaneouslyrecorded data in the training and testing environments (“stereo data”). They differ in that RATZ modifies the incoming feature vectors to a recognition system while STAR modifies the internal representation of speech by the system. We also describe N-CDCN, an improved version of codeword-dependent cepstral normalization (CDCN) which does not require stereo training data but nevertheless achieves performance levels comparable to RATZ and other algorithms that require stereo training. Use of these compensation algorithms significantly reduces the error rates for SPHINX-II. The algorithms are tested in a variety of databases and environmental conditions.

Citations