Allophone clustering for continuous speech recognition

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



31 被引用数 (Scopus)


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.

ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
出版ステータスPublished - 1990
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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