Various Reference Speakers Determination Methods for Embedded Kernel Eigenvoice Speaker Adaptation

Abstract

Recently, we proposed two improvements to the eigenvoice (EV) speaker adaptation using kernel methods: kernel eigenvoice (KEV) speaker adaptation, and embedded kernel eigenvoice (eKEV) speaker adaptation. In both KEV and eKEV adaptation methods, kernel eigenvoices are computed using kernel PCA, and an implicit speaker adapted model is defined as a linear combination of the leading kernel eigenvoices in the kernel-induced feature space. eKEV adaptation further finds an approximate pre-image of the implicit speaker adapted model so that all online kernel evaluations involving any acoustic vectors are eliminated during adaptation and subsequent recognition. The pre-image finding algorithm is cast as a constrained optimization problem using the distances between the expected pre-image and a set of pre-determined reference speakers as constraints. In this paper, we investigate two different ways to determine the reference speakers and the effect of their numbers on the eKEV adaptation performance.