Complex cepstrum for statistical parametric speech synthesis

Ranniery Maia, Masami Akamine, Mark J.F. Gales

研究成果: Article査読

20 被引用数 (Scopus)

抄録

Statistical parametric synthesizers have typically relied on a simplified model of speech production. In this model, speech is generated using a minimum-phase filter, implemented from coefficients derived from spectral parameters, driven by a zero or random phase excitation signal. This excitation signal is usually constructed from fundamental frequencies and parameters used to control the balance between the periodicity and aperiodicity of the signal. The application of this approach to statistical parametric synthesis has partly been motivated by speech coding theory. However, in contrast to most real-time speech coders, parametric speech synthesizers do not require causality. This allows the standard simplified model to be extended to represent the natural mixed-phase characteristics of speech signals. This paper proposes the use of the complex cepstrum to model the mixed phase characteristics of speech through the incorporation of phase information in statistical parametric synthesis. The phase information is contained in the anti-causal portion of the complex cepstrum. These parameters have a direct connection with the shape of the glottal pulse of the excitation signal. Phase parameters are extracted on a frame-basis and are modeled in the same fashion as the minimum-phase synthesis filter parameters. At synthesis time, phase parameter trajectories are generated and used to modify the excitation signal. Experimental results show that the use of such complex cepstrum-based phase features results in better synthesized speech quality. Listening test results yield an average preference of 60% for the system with the proposed phase feature on both female and male voices.

本文言語English
ページ(範囲)606-618
ページ数13
ジャーナルSpeech Communication
55
5
DOI
出版ステータスPublished - 2013 6月
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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