Speech synthesis from surface electromyogram signal

Yuet Ming Lam, Man Wai Mak, Philip Heng Wai Leong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper presents a methodology that uses surface electromyogram (SEMG) signals recorded from the cheek and chin to synthesize speech. Simultaneously recorded speech and SEMG signals are blocked into frames and transformed into features. Linear predictive coding (LPC) and short-time Fourier transform coefficients are chosen as speech and SEMG features respectively. A neural network is applied to convert SEMG features into speech features on a frame-by-frame basis. The converted speech features are used to reconstruct the original speech. Feature selection, conversion methodology and experimental results are discussed. The results show that phoneme-based feature extraction and frame-based feature conversion could be applied to SEMG-based continuous speech synthesis.
Original languageEnglish
Title of host publicationProceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology
Pages749-754
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2005
Event5th IEEE International Symposium on Signal Processing and Information Technology - Athens, Greece
Duration: 18 Dec 200521 Dec 2005

Conference

Conference5th IEEE International Symposium on Signal Processing and Information Technology
Country/TerritoryGreece
CityAthens
Period18/12/0521/12/05

Keywords

  • LPC, short-time Fourier transform
  • Neural networks
  • SEMG
  • Speech synthesis

ASJC Scopus subject areas

  • Engineering(all)

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