Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
High performance connected digit recognition using maximum mutual information estimation
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Improved acoustic modeling with Bayesian learning
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
High performance connected digit recognition using codebook exponents
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
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This paper describes 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 here is a continuation of previous work described in [1, 2]. The experiments described in this paper were all performed on the complete adult portion of the TIDIGITS corpus. The paper describes techniques that allowed us to improve greatly the recognition rate. New results include a 0.28% word error rate and 0.84% string error rate with two models per digit (one for male and one for female speakers) using context dependent discrete HMMs.