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.
|ジャーナル||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|出版物ステータス||Published - 2004 1 1|
|イベント||8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand|
継続期間: 2004 8 9 → 2004 8 13
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)