Decision Tree State Tying Using Cluster Validity Criteria

Jen-Tzung Chien*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Decision tree state tying aims to perform divisive clustering, which can combine the phonetics and acoustics of speech signal for large vocabulary continuous speech recognition. A tree is built by successively splitting the observation frames of a phonetic unit according to the best phonetic questions. To prevent building over-large tree models, the stopping criterion is required to suppress tree growing. Accordingly, it is crucial to exploit the goodness-of-split criteria to choose the best questions for node splitting and test whether the splitting should be terminated or not In this paper, we apply the Hubert's Γ statistic as the node splitting criterion and the T 2-statistic as the stopping criterion. The Hubert's γ statistic sufficiently characterizes the clustering structure in the given data. This cluster validity criterion is adopted to select the best questions to unravel tree nodes. Further, we examine the population closeness of two split nodes with a significance level. The T2-statistic expressed by an F distribution is determined to verify whether the mean vectors of two nodes are close together. The splitting is stopped when verified. In the experiments of Mandarin speech recognition, the proposed methods achieve better syllable recognition rates with smaller tree models compared to the conventional maximum likelihood and minimum description length criteria.

Original languageEnglish
Pages (from-to)182-193
Number of pages12
JournalIEEE Transactions on Speech and Audio Processing
Issue number2
StatePublished - 1 Mar 2005


  • Cluster validity
  • Continuous speech recognition
  • Decision tree
  • F distribution
  • Hubert's Γ statistic
  • Hypothesis test
  • T-statistic

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