Histogram-based spectral equalization for HMM-based speech synthesis using mel-LSP

Yamato Ohtani, Masatsune Tamura, Masahiro Morita, Takehiko Kagoshima, Masami Akamine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper describes a statistical spectral parameter em-phasis technique for HMM-based speech synthesis using mel-scaled line spectral pair (mel-LSP). Spectral parame-ter emphasis is effective for compensating over-smoothed spectra in HMM-based speech synthesis. However, there is no conventional technique that satisfies such require-ments as automatic tuning for different speakers and real-time synthesis for mel-LSP. In the proposed method, the cumulative distribution function (CDF) is calculated from the histogram of spectral parameters that are extracted from training speech data. In the same manner, CDF of spectral parameters that are generated from HMMs is constructed. Then an emphasis rule is trained so that the CDF of generated parameters equals to that of train-ing data. After generating a spectral parameter sequence from HMMs, the spectral parameter sequence is empha-sized by using the rule. Experimental results show that our proposed method improves speech quality.

Original languageEnglish
Title of host publication13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Pages1154-1157
Number of pages4
Publication statusPublished - 2012 Dec 1
Externally publishedYes
Event13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 - Portland, OR, United States
Duration: 2012 Sep 92012 Sep 13

Publication series

Name13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Volume2

Conference

Conference13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
CountryUnited States
CityPortland, OR
Period12/9/912/9/13

Keywords

  • Eel-LSP
  • Hidden Markov model
  • Histogram equalization
  • Parameter emphasis
  • Speech synthesis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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