Speech spectrum conversion based on speaker interpolation and multi-functional representation with weighting by radial basis function networks

Naoto Iwahashi*, Yoshinori Sagisaka

*この研究の対応する著者

研究成果: Article査読

43 被引用数 (Scopus)

抄録

This paper describes a speech spectrum transformation method by interpolating multi-speakers' spectral patterns and multi-functional representation with Radial Basis Function networks. The interpolation is carried out using spectral parameters between pre-stored multiple speakers' utterance data to generate new spectrum patterns. Adaptation to a target speaker can be performed by this interpolation, which uses only a small amount of training data to generate new speech spectrum sequences close to those of the target speaker. Moreover, to obtain more precise adaptation by using a larger amount of training data, the transformation is represented by multiple interpolating functions. The multiple functions' outputs are weighted-summed, using weighting values given by RBF networks. The parameters of this multi-functional transformation are adapted by the gradient descent method. Adaptation experiments were carried out using four pre-stored speakers' data. Using only one word spoken by the target speaker for training, the distance between the target speaker's spectrum and the spectrum generated by the single interpolating function was reduced by about 35% compared with the distance between the target speaker's spectrum and the spectrum of the pre-stored speaker closest to the target. Using ten training words, the reduction rate increased to 48% by the multi-functional transformation.

本文言語English
ページ(範囲)139-151
ページ数13
ジャーナルSpeech Communication
16
2
DOI
出版ステータスPublished - 1995 2
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • モデリングとシミュレーション
  • 通信
  • 言語および言語学
  • 言語学および言語
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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