Publication

Robust triphone mapping for acoustic modeling

Hervé Bourlard
2012
Conference Papers

Abstract

In this paper we revisit the recently proposed triphone mapping as an alternative to decision tree state clustering. We generalize triphone mapping to Kullback-Leibler based hidden Markov models for acoustic modeling and propose a modified training procedure for the Gaussian mixture model based acoustic modeling. We compare the triphone mapping to decision tree state clustering on the Wall Street journal task as well as in the context of an under-resourced language by using Greek data from the SpeechDat(II) corpus. Experiments reveal that triphone mapping has the best overall performance and is robust against varying the acoustic modeling technique as well as variable amounts of training data.

Official source

https://infoscience.epfl.ch/entities/publication/aa2cba79-8696-4644-9b00-06ce221f8d5d

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Hervé Bourlard
2012
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/aa2cba79-8696-4644-9b00-06ce221f8d5d

About this result

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