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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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