Fast Likelihood Computation Methods For Continuous Mixture Densities In Large Vocabulary Speech Recognition (1997)
| Venue: | In Proc. of the European Conf. on Speech Communication and Technology |
| Citations: | 11 - 8 self |
BibTeX
@INPROCEEDINGS{Ortmanns97fastlikelihood,
author = {Stefan Ortmanns and Hermann Ney and Thorsten Firzlaff},
title = {Fast Likelihood Computation Methods For Continuous Mixture Densities In Large Vocabulary Speech Recognition},
booktitle = {In Proc. of the European Conf. on Speech Communication and Technology},
year = {1997},
pages = {139--142}
}
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Abstract
This paper studies algorithms for reducing the computational effort of the mixture density calculations in HMM-based speech recognition systems. These likelihood calculations take about 70 \Gamma 85% of the total recognition time in the RWTH system for large vocabulary continuous speech recognition. To reduce the computational cost of the likelihood calculations, we investigate several space partitioning methods. A detailed comparison of these techniques is given on the North American Business Corpus (NAB'94) for a 20 000word task. As a result, the so-called projection search algorithm in combination with the VQ method reduces the cost of likelihood computation by a factor of about 8 with no significant loss in the word recognition accuracy. 1.
Citations
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| 10 | Closest point search in high dimensions - Nene, Nayar - 1996 |
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