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Title:Average-Voice-based Speech Synthesis using HSMM-based Speaker Adaptation and Adaptive Training
Authors: Junichi Yamagishi ; Takao Kobayashi
Date:Feb 2007
Publication Title:IEICE TRANSACTIONS on Information and Systems
Publisher:Oxford University Press
Publication Type:Journal Article Publication Status:Published
Volume No:E90-D Page Nos:533-543
DOI:10.1093/ietisy/e90-d.2.533 ISBN/ISSN:0916-8532
In speaker adaptation for speech synthesis, it is desirable to convert both voice characteristics and prosodic features such as F0 and phone duration. For simultaneous adaptation of spectrum, F0 and phone duration within the HMM framework, we need to transform not only the state output distributions corresponding to spectrum and F0 but also the duration distributions corresponding to phone duration. However, it is not straightforward to adapt the state duration because the original HMM does not have explicit duration distributions. Therefore, we utilize the framework of the hidden semi-Markov model (HSMM), which is an HMM having explicit state duration distributions, and we apply an HSMM-based model adaptation algorithm to simultaneously transform both the state output and state duration distributions. Furthermore, we propose an HSMM-based adaptive training algorithm to simultaneously normalize the state output and state duration distributions of the average voice model. We incorporate these techniques into our HSMM-based speech synthesis system, and show their effectiveness from the results of subjective and objective evaluation tests.
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Bibtex format
author = { Junichi Yamagishi and Takao Kobayashi },
title = {Average-Voice-based Speech Synthesis using HSMM-based Speaker Adaptation and Adaptive Training},
journal = {IEICE TRANSACTIONS on Information and Systems},
publisher = {Oxford University Press},
year = 2007,
month = {Feb},
volume = {E90-D},
pages = {533-543},
doi = {10.1093/ietisy/e90-d.2.533},
url = {},

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