Probabilistic Concatenation Modeling for Corpus-Based Speech Synthesis
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- SAKAI Shinsuke
- Academic Center for Computing and Media Studies, Kyoto University
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- KAWAHARA Tatsuya
- Academic Center for Computing and Media Studies, Kyoto University
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- KAWAI Hisashi
- Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology
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Abstract
The measure of the goodness, or inversely the cost, of concatenating synthesis units plays an important role in concatenative speech synthesis. In this paper, we present a probabilistic approach to concatenation modeling in which the goodness of concatenation is measured by the conditional probability of observing the spectral shape of the current candidate unit given the previous unit and the current phonetic context. This conditional probability is modeled by a conditional Gaussian density whose mean vector has a form of linear transform of the past spectral shape. Decision tree-based parameter tyingis performed to achieve robust trainingthat balances between model complexity and the amount of training data available. The concatenation models are implemented for a corpus-based speech synthesizer, and the effectiveness of the proposed method wasconfirmed by an objective evaluation as well as a subjective listening test. We also demonstrate that the proposed method generalizes some popular conventional methods in that those methods can be derived as the special cases of the proposed method.
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E94-D (10), 2006-2014, 2011
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390282679355544448
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- NII Article ID
- 10030193499
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- NII Book ID
- AA10826272
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed