Natural speech synthesis based on hybrid approach with candidate expansion and verification

Chung-Hsien Wu, Yi Chin Huang, Shih Lun Lin, Chia Ping Chen

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

2 Citations (Scopus)

Abstract

A hybrid Mandarin speech synthesis system combining concatenation-based and model-based methodology is investigated in this research. To effectively exploit a small-size corpus, the candidate sets for unit selection are expanded via clusters based on articulatory features (AF), which are estimated as the outputs of an artificial neural network. This is followed by a filtering operation incorporating residual compensation, to remove unsuitable units. Given an input text, an optimal unit sequence is decided by the minimization of a total cost, which depends on the spectral features, contextual articulatory features, formants, and pitch values. Furthermore, prosodic word verification is integrated to check the smoothness of the output speech. The units failing to pass the prosodic word verification are replaced by model-based synthesized units for better speech quality. Objective and subjective evaluations have been conducted. Comparisons among the proposed method, the HMM-based method, and the conventional hybrid method clearly show that candidate set expansion based on articulatory features lead to more units suitable for selection, and the verification process is effective in improving the naturalness of the output speech.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages250-254
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 2014 May 42014 May 9

Publication series

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

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period14-05-0414-05-09

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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