Discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition
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In this paper we propose discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition tasks. After presenting our hierarchical modeling framework, we describe how the models can be generated with either minimum classification error or large-margin training. Experiments on a large vocabulary lecture transcription task show that the hierarchical model can yield more than 1.0% absolute word error rate reduction over non-hierarchical models for both kinds of discriminative training.
Date issued
2009-05Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Hung-An Chang, and J.R. Glass. “Discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. 2009. 4481-4484. © Copyright 2009 IEEE
Version: Final published version
Other identifiers
INSPEC Accession Number: 10701095
ISBN
978-1-4244-2353-8
ISSN
1520-6149