Presentation	1999/12/20 AStudyonTree-BasedClusteringforSpeaker-IndependentGaussianMixtureHMMs Tsuneo Kato, Shingo Kuroiwa, Tohru Shimizu, Norio Higuchi,
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Abstract(in English)	Tree-based clustering is an effective method to share HMM states by clustering triphones based on phonetic questions. Previous researches on this method have been made on HMMs of single Gaussian output distributions due to computational restrictions. However, single Gaussian HMMs may not be sufficient to create appropriate topology (i.e. HMM state sharing). Furthermore, a significant amount of time is required to obtain Gaussian mixture HMMs for repetitive distribution splitting and embedded training. In this paper, we propose a tree-based clustering for Gaussian mixture HMMs based on distribution clustering. This method achieved 67% reduction on training time and 1-2% improvement in phoneme accuracy
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Paper #	NLC99-99
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Committee	NLC
Conference Date	1999/12/20(1days)
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Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
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Title (in English)	AStudyonTree-BasedClusteringforSpeaker-IndependentGaussianMixtureHMMs
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1st Author's Name	Tsuneo Kato
1st Author's Affiliation	()
2nd Author's Name	Shingo Kuroiwa
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3rd Author's Name	Tohru Shimizu
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4th Author's Name	Norio Higuchi
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Date	1999/12/20
Paper #	NLC99-99
Volume (vol)	vol.99
Number (no)	523
Page	pp.pp.-
#Pages	6
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