Title :
Joint quantizer design and parameter estimation for discrete hidden Markov models
Author :
Ostendorf, Mari ; Rohlicek, J.
Author_Institution :
Dept. of Electr. Comput. Sci., Boston Univ., MA, USA
Abstract :
An approach that involves designing a vector quantizer to maximize the mutual information between the hidden Markov model (HMM) states and the quantized observations is presented. The iterative design of the quantizer and the HMM parameters is shown to be jointly a maximum-likelihood estimate. Methodologies for using the maximum mutual information (MMI) criterion for quantizer design are described, and some initial results are presented to demonstrate that the MMI criterion yields improved speech recognition performance
Keywords :
Markov processes; analogue-digital conversion; iterative methods; parameter estimation; probability; speech recognition; discrete hidden Markov models; iterative design; maximum mutual information; maximum-likelihood estimate; parameter estimation; quantizer; speech recognition; vector quantizer; Algorithm design and analysis; Clustering algorithms; Distortion measurement; Hidden Markov models; Maximum likelihood estimation; Mutual information; Parameter estimation; Partitioning algorithms; Quantization; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115865
