A speaker adaptation technique for mrhsmm-based style control of synthetic speech

Takashi Nose, Yoichi Kato, Takao Kobayashi

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

14 Citations (Scopus)

Abstract

This paper describes a speaker adaptation technique for style control based on multiple regression hidden semi-Markov model (MRHSMM). In the MRHSMM-based style control technique, when available training data is very small. the resultant model would produce unnatural sounding speech. To overcome this problem, we propose a model adaptation technique for MRHSMM, which is similar to the MLLR adaptation technique used in speech recognition and speech synthesis. We formulate the model adaptation problem for MRHSMM based on a linear transformation framework and derive re-estimation formulas for transformation matrices in ML sense. We also describe the results of subjective evaluation tests.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIV833-IV836
DOIs
Publication statusPublished - 2007 Aug 6
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 2007 Apr 152007 Apr 20

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period07/4/1507/4/20

Keywords

  • Expressive speech synthesis
  • Hidden Markov model
  • MLLR
  • Speaker adaptation
  • Style control

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Nose, T., Kato, Y., & Kobayashi, T. (2007). A speaker adaptation technique for mrhsmm-based style control of synthetic speech. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. IV833-IV836). [4218230] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4). https://doi.org/10.1109/ICASSP.2007.367042