Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Efficient cepstral normalization for robust speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
Comparative experiments on large vocabulary speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
1993 benchmark tests for the ARPA spoken language program
HLT '94 Proceedings of the workshop on Human Language Technology
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This paper describes a series of cepstral-based compensation procedures that render the SPHINX-II system more robust with respect to acoustical environment. The first algorithm, phone-dependent 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, as well as the use of reduced-bandwidth frequency analysis to process telephone-bandwidth speech. 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, in both development test sets and in the context of the 1993 ARPA CSR evaluations.