DocumentCode :
2939563
Title :
Hierarchical mixtures of experts methodology applied to continuous speech recognition
Author :
Zhao, Ying ; Schwartz, Richard ; Sroka, Jason ; Makhoul, John
Author_Institution :
BBN Syst. & Technol., Cambridge, MA, USA
Volume :
5
fYear :
1995
fDate :
9-12 May 1995
Firstpage :
3443
Abstract :
We incorporate the hierarchical mixtures of experts (HME) method of probability estimation, developed by Jordan (see Neural Computation, 1994), into an HMM-based continuous speech recognition system. The resulting system can be thought of as a continuous-density HMM system, but instead of using Gaussian mixtures, the HME system employs a large set of hierarchically organized but relatively small neural networks to perform the probability density estimation. The hierarchical structure is reminiscent of a decision tree except for two important differences: each “expert” or neural net performs a “soft” decision rather than a hard decision, and, unlike ordinary decision trees, the parameters of all the neural nets in the HME are automatically trainable using the EM algorithm. We report results on the ARPA 5,000-word and 40,000-word Wall Street Journal corpus using HME models
Keywords :
decision theory; estimation theory; hidden Markov models; neural nets; speech recognition; EM algorithm; HMM-based continuous speech recognition system; Wall Street Journal corpus; continuous speech recognition; continuous-density HMM system; decision trees; hierarchical mixtures of experts; hierarchical network structure; neural networks; probability density estimation; soft decision; Classification tree analysis; Decision trees; Equations; Hidden Markov models; Large-scale systems; Neural networks; Speech recognition; State estimation; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
ISSN :
1520-6149
Print_ISBN :
0-7803-2431-5
Type :
conf
DOI :
10.1109/ICASSP.1995.479726
Filename :
479726
Link To Document :
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