Vowel imitation using vocal tract model and recurrent neural network

Hisashi Kanda*, Tetsuya Ogata, Kazunori Komatani, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A vocal imitation system was developed using a computational model that supports the motor theory of speech perception. A critical problem in vocal imitation is how to generate speech sounds produced by adults, whose vocal tracts have physical properties (i.e., articulatory motions) differing from those of infants' vocal tracts. To solve this problem, a model based on the motor theory of speech perception, was constructed. Applying this model enables the vocal imitation system to estimate articulatory motions for unexperienced speech sounds that have not actually been generated by the system. The system was implemented by using Recurrent Neural Network with Parametric Bias (RNNPB) and a physical vocal tract model, called Maeda model. Experimental results demonstrated that the system was sufficiently robust with respect to individual differences in speech sounds and could imitate unexperienced vowel sounds.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
ページ222-232
ページ数11
PART 2
DOI
出版ステータスPublished - 2008 10 23
外部発表はい
イベント14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
継続期間: 2007 11 132007 11 16

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 2
4985 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
国/地域Japan
CityKitakyushu
Period07/11/1307/11/16

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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