Speaker adaptation based on speaker-dependent eigenphone estimation

Abstract

Based on speaker dependent eigenphone estimation, a novel speaker adaptation technique is proposed in this paper. Different from conventional speaker adaptation approaches, the proposed method explicitly models the phone variations for each speaker through subspace modeling in the phone space. The phone coordinate, which is shared by all speakers, contains correlation information between different phones. During speaker adaptation, two schemes for estimation of the new speaker specific phone variation bases (namely eigenphones) are derived under maximum likelihood (ML) criterion and maximum a posteriori (MAP) criterion respectively. Supervised speaker adaptation experiments on a Mandarin Chinese continuous speech recognition task show that the new method outperforms both eigenvoice and maximum likelihood linear regression (MLLR) methods when sufficient adaptation data is available.