Improved acoustic modeling for speaker independent large vocabulary continuous speech recognition
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 161-164 vol.1
- https://doi.org/10.1109/icassp.1991.150302
Abstract
The authors report recent improvements to an HMM (hidden, Markov model)-based, continuous speech recognition system. These advances, which include the incorporation of interword context-dependent units and position-dependent units and an improved feature analysis, lead to a recognition system which gives a 95% word accuracy and 75% sentence accuracy for speaker independent recognition of the 1000-word, DARPA resource management task using the standard word pair grammar (with a perplexity of about 60). With the improved acoustic modeling of subword units, the overall error rate reduction was over 42% compared with the performance results reported in the baseline system. The best results obtained so far using the word pair grammar gave 95.2% average word accuracy for the three DARPA evaluation sets.Keywords
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