Training a supra-segmental parametric F0 model without interpolating F0

Javier Latorre, Mark J.F. Gales, Kate Knill, Masami Akamine

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

1 Citation (Scopus)

Abstract

Combining multiple intonation models at different linguistic levels is an effective way to improve the naturalness of the predicted F0. In many of these approaches, the intonation models for suprasegmental levels are based on a parametrization of the log-F0 contours over the units of that level. However, many of these parametrisations are not stable when applied to discontinuous signals. Therefore, the F0 signal has to be interpolated. These interpolated values introduce a distortion in the coefficients that degrades the quality of the model. This paper proposes two methods that eliminate the need for such interpolation, one based on regularization and the other on factor analysis. Subjective evaluations show that, for a Discrete-cosine-transform (DCT) syllable-level model, both approaches result in a significant improvement w.r.t. a baseline using interpolated F0. The approach based on regularization yields the best results.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages6880-6884
Number of pages5
DOIs
Publication statusPublished - 2013 Oct 18
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 2013 May 262013 May 31

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period13/5/2613/5/31

Keywords

  • F0 interpolation
  • factor analysis
  • intonation
  • regularization
  • speech synthesis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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