Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMNET


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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/7945

Title:	Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMNET
Authors:	Alexandre Girardi Harald Singer Kiyohiro Shikano Satoshi Nakamura
Issue Date:	Sep-1997
Start page:	119
End page:	122
Abstract:	This paper describes a new approach to ML-SSS (Maximum Likelihood Successive State Splitting) algorithm that uses tied- mixture representation of the output probability density function instead of a single Gaussian during the splitting phase of the ML-SSS algorithm. The tied-mixture representation results in a better state split gain, because it is able to measure diferences in the phoneme environment space that ML-SSS can not. With this more informative gain the new algorithm can choose a better split state and corresponding data. Phoneme clustering experiments were conducted which lead up to 38% of error reduction if compared to the ML-SSS algorithm.
Description:	EUROSPEECH1997: the 5th European Conference on Speech Communication and Technology , September 22-25, 1997, Rhodes, Greece.
URI:	http://hdl.handle.net/10061/7945
ISSN:	1018-4074
Rights:	Copyright 1997 ISCA
Text Version:	Publisher
Appears in Collections:	情報科学研究科 / Graduate School of Information Science

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