Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA

Brian Mak, Simon Ho and James T. James

Abstract: Recently, we proposed an improvement to the eigenvoice (EV) speaker adaptation called kernel eigenvoice (KEV) speaker adaptation. In KEV adaptation, eigenvoices are computed using kernel PCA, and a new speaker's adapted model is implicitly computed in the kernel-induced feature space. Due to many online kernel evaluations, both adaptation and subsequent recognition of KEV adaptation are slower than EV adaptation. In this paper, we eliminate all online kernel computations by finding an approximate pre-image of the implicit adapted model found by KEV adaptation. Furthermore, the two steps of finding the implicit adapted model and its approximate pre-image are integrated by embedding the kernel PCA procedure in our new embedded kernel eigenvoice (eKEV) speaker adaptation method. When tested in an TIDIGITS task with less than 10s of adaptation speech, eKEV adaptation obtained a speedup of 6-14 times in adaptation and 136 times in recognition over KEV adaptation with 12-13% relative improvement in recognition accuracy.

Proceedings of the International Conference on Spoken Language Processing (INTERSPEECH2004-ICSLP), vol 4, pp.2913-2916, Jeju, Korea, October 2004.

PDF: http://www.cs.ust.hk/~jamesk/papers/icslp04.pdf

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