ImprovementofMLLRAlgorithmforRapidSpeakerAdaptationandReductionofComputation 


Vol. 29,  No. 1, pp. 65-71, Jan.  2004


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  Abstract

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker
Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

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  Cite this article

[IEEE Style]

J. Kim and J. Chung, "ImprovementofMLLRAlgorithmforRapidSpeakerAdaptationandReductionofComputation," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 1, pp. 65-71, 2004. DOI: .

[ACM Style]

Ji-Un Kim and Jae-Ho Chung. 2004. ImprovementofMLLRAlgorithmforRapidSpeakerAdaptationandReductionofComputation. The Journal of Korean Institute of Communications and Information Sciences, 29, 1, (2004), 65-71. DOI: .

[KICS Style]

Ji-Un Kim and Jae-Ho Chung, "ImprovementofMLLRAlgorithmforRapidSpeakerAdaptationandReductionofComputation," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 1, pp. 65-71, 1. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880