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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/8071

Title: On the State Definition for a Trainable Excitation Model in HMM-based Speech Synthesis
Authors: Ranniery Maia
Tomoki Toda
Keiichi Tokuda
Shinsuke Sakai
Satoshi Nakamura
Keywords: Speech processing
speech synthesis
hidden Markov models
digital filters
Issue Date: Mar-2008
Publisher: IEEE
Start page: 3965
End page: 3968
Abstract: One of the issues of speech synthesizers based on hidden Markov models concerns the vocoded quality of the synthesized speech. From the principle of analysis-by-synthesis speech coders a trainable excitation model has been proposed to improve naturalness, where the method consists in the design of a set of state-dependent filters in a way to minimize the distortion between residual and synthetic excitation. Although this approach seems successful, state definition still represents an open issue. This paper describes a method for state definition wherein bottom-up clustering is performed on full context decision trees, using the likelihood of the residual database as merging criterion. Experiments have shown that improvement on residual modeling through better filter design can be achieved.
Description: ICASSP2008: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 30 - April 4, 2008, Las Vegas, Nevada, USA.
URI: http://hdl.handle.net/10061/8071
ISBN: 9781424414833
ISSN: 1520-6149
Rights: Copyright 2008 IEEE
Text Version: Publisher
Publisher DOI: 10.1109/ICASSP.2008.4518522
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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