Allophone clustering for continuous speech recognition

Kai Fu Lee*, Satoru Hayamizu, Hsiao Wuen Hon, Cecil Huang, Jonathan Swartz, Robert Weide

*この研究の対応する著者

研究成果査読

31 被引用数 (Scopus)

抄録

Two methods are presented for subword clustering. The first method is an agglomerative clustering algorithm. This method is completely data-driven and finds clusters without any external guidance. The second method uses decision trees for clustering. This method uses an expert-generated list of questions about contexts and recursively selects the most appropriate question to split the allophones. Preliminary results showed that when the training set has a good coverage of the allophonic variations in the test set, both methods are capable of high-performance recognition. However, under vocabulary-independent conditions, the method using tree-based allophones outperformed agglomerative clustering because of its superior generalization capability.

本文言語English
ページ(範囲)749-752
ページ数4
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2
出版ステータスPublished - 1990
外部発表はい
イベント1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
継続期間: 1990 4 31990 4 6

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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