한국음향학회:학술대회논문집 (Proceedings of the Acoustical Society of Korea Conference)
- 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
- /
- Pages.1052-1057
- /
- 1994
DYNAMICALLY LOCALIZED SELF-ORGANIZING MAP MODEL FOR SPEECH RECOGNITION
초록
Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.
키워드