A new adaptation method for speaker-model creation in high-level speaker verification

Research has shown that speaker verification based on highlevel speaker features requires long enrollment utterances to be reliable. However, in practical speaker verification, it is common to model speakers based a limited amount of enrollment data. To minimize the undesirable effect of insufficient enrollment data on system performance, this paper proposes a new adaptation method for creating speaker models based on high-level features. Different from conventional methods, the proposed adaptation method not only adapts the phoneme-dependent background model but also the phoneme-independent speaker model. The amount of adaptation in the latter is adjusted by a proportional factor derived from the phoneme-independent background models. The proposed method was compared with traditional MAP adaptation under the NIST2000 SRE framework. Experimental results show that the proposed method can solve the data-spareness problem effectively and achieves a better performance when compare with traditional MAP adaptation.