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|a Chang, Hung-An
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Glass, James R.
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|a Chang, Hung-An
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|a Glass, James R.
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|a Glass, James R.
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|a Discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition
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|b Institute of Electrical and Electronics Engineers,
|c 2011-01-14T13:39:59Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/60562
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|a 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.
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|a Taiwan Merit Scholarship (Number NSC-095- SAF-I-564-040-TMS)
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|a en_US
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|a Article
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|t IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009.
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