Word accent patterns modelling by concatenation of mora hidden markov models

Takashi Yoshimura, Satoru Hayamizu, Kazuyo Tanaka

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

This paper describes a new method for representing and identifying isolated word accent patterns. The word accent patterns are represented by concatenation of mora Hidden Markov Models for fundamental frequency feature sequences. The mora HMMs are trained by accent-related features automatically extracted without manual correction from the speech wave. These algorithms are evaluated using a speech sample set consisting of 10 speakers' 121 words, where word accent patterns are classified by listening. All words have 4 mora and are selected from a phonetically balanced word set. Two experiments are performed to compare the automatically extracted features with the features manually corrected in unvoired parts of the speech wave. Little difference was found in the results obtained using the two different features, indicating that the mora HMMs using automatically extracted features are useful for representing and identifying word accent patterns.

Original languageEnglish
Article number389353
Pages (from-to)I69-I72
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
DOIs
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: 1994 Apr 191994 Apr 22

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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