Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

Junichi Yamagishi, Simon King, Steve Renals, Takashi Nose, Heiga Zen, Keiichi Tokuda, Zhen Hua Ling, Tomoki Toda

Research output: Contribution to journalArticlepeer-review

144 Citations (Scopus)


This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called “HTS-2007,” employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.

Original languageEnglish
Pages (from-to)1208-1230
Number of pages23
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number6
Publication statusPublished - 2009 Aug
Externally publishedYes


  • Average voice
  • HMM Speech Synthesis System
  • HMM-based speech synthesis
  • HTS
  • speaker adaptation
  • speech synthesis
  • voice conversion

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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