Adaptive Training for Voice Conversion Based on Eigenvoices

DOI IR HANDLE Web Site 6 Citations 18 References Open Access
  • OHTANI Yamato
    Graduate School of Information Science, Nara Institute of Science and Technology
  • TODA Tomoki
    Graduate School of Information Science, Nara Institute of Science and Technology
  • SARUWATARI Hiroshi
    Graduate School of Information Science, Nara Institute of Science and Technology
  • SHIKANO Kiyohiro
    Graduate School of Information Science, Nara Institute of Science and Technology

Search this article

Abstract

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.

Journal

Citations (6)*help

See more

References(18)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top