High performance connected digit recognition using codebook exponents

The authors describe the latest developments by the speech research group at CRIM in speaker-independent connected digit recognition, using hidden Markov models (HMMs) trained with maximum mutual information estimation (MMIE). The work presented is a continuation of work previously described by the authors (see Proc. 1991 IEEE Inf. Conf. on Acoust. Speech and Sign. Process., pp.533-536). The main differences are: (1) use of the 20-kHz TI/NIST corpus available on CD-ROM (instead of the 10-kHz distribution tape), (2) use of word models (instead of sub-word units), (3) addition of second derivative parameters, and (4) a more elaborate training procedure for codebook exponents. The experiments described were all performed on the complete adult portion of the corpus. The baseline system, using discrete HMMs and MMIE, has a 0.67% word error rate and a 2.03% string error rate. The authors describe techniques that allowed them to improve greatly the recognition rate