Advanced Search
Japanese | English

naistar (NAIST Academic Repository) >
学術リポジトリ naistar / NAIST Academic Repository naistar >
国際会議発表論文 / Proceedings >
情報科学研究科 / Graduate School of Information Science >

Please use this identifier to cite or link to this item:

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.
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

Files in This Item:

File SizeFormat
EUROSPEECH_1997_119.pdf503.31 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Copyright (c) 2007-2012 Nara Institute of Science and Technology All Rights Reserved.
DSpace Software Copyright © 2002-2010  Duraspace - Feedback