Regression-based clustering for hierarchical pitch conversion

Chung Han Lee, Chi Chun Hsia, Chung Hsien Wu, Mai Chun Lin

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


This study presents a hierarchical pitch conversion method using regression-based clustering for conversion function modeling. The pitch contour of a speech utterance is first extracted and decomposed into sentence-, wordand sub-syllable-level features in a top-down mechanism. The pair-wise source and target pitch feature vectors at each level are then clustered to generate the pitch conversion function. Regression-based clustering, which clusters the feature vectors to achieve a minimum conversion error between the predicted and the real feature vectors is proposed for conversion function generation. A classification and regression tree (CART), incorporating linguistic, phonetic and source prosodic features, is adopted to select the most suitable function for pitch conversion. Several objective and subjective evaluations were conducted and the comparison results to the GMMbased methods for pitch conversion confirm the performance of the proposed regression-based clustering approach.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Number of pages4
Publication statusPublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan
Duration: 2009 Apr 192009 Apr 24

Publication series

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


Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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