Bayesian modelling of the speech spectrum using mixture of Gaussians

Parham Zolfaghari*, Shinji Watanabe, Atsushi Nakamura, Shigeru Katagiri

*この研究の対応する著者

研究成果: Conference article査読

14 被引用数 (Scopus)

抄録

This paper presents a method for modelling the speech spectral envelope using a mixture of Gaussians (MOG). A novel variational Bayesian (VB) framework for Gaussian mixture modelling of a histogram enables the derivation of an objective function that can be used to simultaneously optimise both model parameter distributions and model structure. A histogram representation of the STRAIGHT spectral envelope, which is free of glottal excitation information, is used for parametrisation using this MOG model. This results in a parameterisation scheme that purely models the vocal tract resonant characteristics. Maximum likelihood (ML) and variational Bayesian (VB) solutions of the mixture model on histogram data are found using an iterative algorithm. A comparison between ML-MOG and VB-MOG spectral modelling is carried out using spectral distortion measures and mean opinion scores (MOS). The main advantages of VB-MOG highlighted in this paper include better modelling using fewer Gaussians in the mixture resulting in better correspondence of Gaussians and formant-like peaks, and an objective measure of the number of Gaussians required to best fit the spectral envelope.

本文言語English
ページ(範囲)I553-I556
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
1
出版ステータスPublished - 2004 9月 28
外部発表はい
イベントProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
継続期間: 2004 5月 172004 5月 21

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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