Online learning of large margin hidden Markov models for automatic speech recognition

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Summary

This dissertation explores the use of sequential, mistake-driven updates for online learning and acoustic feature adaptation in large margin HMMs, and finds that online updates for large margin training not only converge faster than analogous batch optimizations, but also yield lower phone error rates than approaches that do not attempt to enforce a large margin.

Type
article
Published
2011-01-01
Cited by
3
References
127

Keywords

Margin (machine learning), Discriminative model, Hidden Markov model, Computer science, Speech recognition

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