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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/8166

Title: A Decision Tree-Based Clustering Approach to State Definition in an Excitation Modeling Framework for HMM-Based Speech Synthesis
Authors: Ranniery Maia
Tomoki Toda
Keiichi Tokuda
Shinsuke Sakai
Satoshi Nakamura
Keywords: speech synthesis
HMM-based speech synthesis
decision tree-based clustering
residual modeling
Issue Date: Sep-2009
Start page: 1783
End page: 1786
Abstract: This paper presents a decision tree-based algorithm to cluster residual segments assuming an excitation model based on statedependent filtering of pulse train and white noise. The decision tree construction principle is the same as the one applied to speech recognition. Here parent nodes are split using the residual maximum likelihood criterion. Once these excitation decision trees are constructed for residual signals segmented by full context models, using questions related to the full context of the training sentences, they can be utilized for excitation modeling in speech synthesis based on hidden Markov models (HMM). Experimental results have shown that the algorithm in question is very effective in terms of clustering residual signals given segmentation, pitch marks and full context questions, resulting in filters with good residual modeling properties.
Description: INTERSPEECH2009: 10th Annual Conference of the International Speech Communication Association, September 6-10, 2009, Brighton, UK.
URI: http://hdl.handle.net/10061/8166
Rights: Copyright 2009 ISCA
Text Version: Publisher
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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