Signal Processing for Robust Speech Recognition

This paper describes several new cepstral-based compensation procedures that render the SPHINX-II system more robust with respect to acoustical environment. The first algorithm, phonedependent cepstral compensation, is similar in concept to the previously-described MFCDCN method, except that cepstral compensation vectors are selected according to the current phonetic hypothesis, rather than on the basis of SNR or VQ codeword identity. We also describe two procedures to accomplish adaptation of the VQ codebook for new environments. Use of the various compensation algorithms in consort produces a reduction of error rates for SPHINX-II by as much as 40 percent relative to the rate achieved with cepstral mean normalization alone.