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CSL
2004
Springer

Factor analysed hidden Markov models for speech recognition

14 years 13 days ago
Factor analysed hidden Markov models for speech recognition
Recently various techniques to improve the correlation model of feature vector elements in speech recognition systems have been proposed. Such techniques include semi-tied covariance HMMs and systems based on factor analysis. All these schemes have been shown to improve the speech recognition performance without dramatically increasing the number of model parameters compared to standard diagonal covariance Gaussian mixture HMMs. This paper introduces a general form of acoustic model, the factor analysed HMM. A variety of configurations of this model and parameter sharing schemes, some of which correspond to standard systems, were examined. An EM algorithm for the parameter optimisation is presented along with a number of methods to increase the efficiency of training. The performance of FAHMMs on medium to large vocabulary continuous speech recognition tasks was investigated. The experiments show that without elaborate complexity control an equivalent or better performance compared to...
Antti-Veikko I. Rosti, M. J. F. Gales
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CSL
Authors Antti-Veikko I. Rosti, M. J. F. Gales
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