Adaptive Training for Voice Conversion Based on Eigenvoices
| Autor: | Hiroshi Saruwatari, Tomoki Toda, Yamato Ohtani, Kiyohiro Shikano |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
voice conversion
Computer science Speech recognition Process (computing) Speech synthesis adaptive training computer.software_genre Mixture model symbols.namesake Gaussian mixture model eigenvoice speech synthesis Artificial Intelligence Hardware and Architecture symbols Computer Vision and Pattern Recognition Electrical and Electronic Engineering Adaptation (computer science) Likelihood function Gaussian process computer Software |
| Zdroj: | IEICE Transactions on Information and Systems. :1589-1598 |
| ISSN: | 1745-1361 0916-8532 |
| DOI: | 10.1587/transinf.e93.d.1589 |
| Popis: | In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker's voice into an arbitrary target speaker's voice. An eigenvoice Gaussian mixture model (EV-GMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored target speakers. The EV-GMM can be adapted to new target speakers using only a few of their arbitrary utterances by estimating a small number of adaptive parameters. In the adaptation process, several parameters of the EV-GMM to be fixed for different target speakers strongly affect the conversion performance of the adapted model. In order to improve the conversion performance in one-to-many EVC, we propose an adaptive training method of the EV-GMM. In the proposed training method, both the fixed parameters and the adaptive parameters are optimized by maximizing a total likelihood function of the EV-GMMs adapted to individual pre-stored target speakers. We conducted objective and subjective evaluations to demonstrate the effectiveness of the proposed training method. The experimental results show that the proposed adaptive training yields significant quality improvements in the converted speech. |
| Databáze: | OpenAIRE |
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