Kernel Eigenspace-Based MLLR Adaptation

In this paper, we propose an application of kernel methods for fast speaker adaptation based on kernelizing the eigenspace-based maximum-likelihood linear regression adaptation method. We call our new method "kernel eigenspace-based maximum-likelihood linear regression adaptation" (KEMLLR)...

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Veröffentlicht in:	IEEE transactions on audio, speech, and language processing speech, and language processing, 2007-03, Vol.15 (3), p.784-795
Hauptverfasser:	Mak, B.K.-W., Hsiao, R.W.-H.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation Adaptation model Applied sciences Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization Coding, codes composite kernels eigenspace-based maximum-likelihood linear regression (MLLR) adaptation eigenvoice speaker adaptation embedded kernel eigenvoice adaptation Exact sciences and technology Hidden Markov models Information, signal and communications theory Kernel kernel eigenvoice adaptation kernel principal component analysis (PCA) Kernels Linear regression Mathematical analysis Mathematical models Matrices Maximum likelihood decoding Maximum likelihood estimation Maximum likelihood linear regression Principal component analysis Regression Signal and communications theory Signal processing Speech Speech processing Speech recognition Studies Telecommunications and information theory Transformations Vectors
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