Automatic sound-imitation word recognition from environmental sounds focusing on ambiguity problem in determining phonemes

Kazushi Ishihara, Tomohiro Nakatani, Tetsuya Ogata, Hiroshi G. Okuno

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)

Abstract

Sound-imitation words (SIWs), or onomatopoeia, are important for computer human interactions and the automatic tagging of sound archives. The main problem in automatic SIW recognition is ambiguity in the determining phonemes, since different listener hears the same environmental sound as a different SIW even under the same situation. To solve this problem, we designed a set of new phonemes, called the basic phoneme-group set, to represent environmental sounds in addition to a set of the articulation-based phoneme-groups. Automatic SIW recognition based on Hidden Markov Model (HMM) with the basic phoneme-groups is allowed to generate plural SIWs in order to absorb ambiguities caused by listener- and situation-dependency. Listening experiments with seven subjects proved that automatic SIW recognition based on the basic phoneme-groups outperformed that based on the articulation-based phoneme-groups and that based on Japanese phonemes. The proposed system proved more adequate to use computer interactions.

Original languageEnglish
Pages (from-to)909-918
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3157
DOIs
Publication statusPublished - 2004 Jan 1
Externally publishedYes
Event8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand
Duration: 2004 Aug 92004 Aug 13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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