Factor analyzed HMM topology for speech recognition

Chuan Wei Ting*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


This paper presents a new factor analyzed (FA) similarity measure between two Gaussian mixture models (GMMs). An adaptive hidden Markov model (HMM) topology is built to compensate the pronunciation variations in speech recognition. Our idea aims to evaluate whether the variation of a HMM state from new speech data is significant or not and judge if a new state should be generated in the models. Due to the effectiveness of FA data analysis, we measure the GMM similarity by estimating the common factors and specific factors embedded in the HMM means and variances. Similar Gaussian densities are represented by the common factors. Specific factors express the residual of similarity measure. We perform a composite hypothesis test due to common factors as well as specific factors. An adaptive HMM topology is accordingly established from continuous collection of training utterances. Experiments show that the proposed FA measure outperforms other measures with comparable size of parameters.

Original languageEnglish
Pages (from-to)1415-1418
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 26 Nov 2009
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009


  • Factor analysis
  • HMM topology
  • Similarity measure
  • Speech recognition

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