A new adaptation approach to high-level speaker-model creation in speaker verification

Research has shown that speaker verification based on high-level speaker features requires long enrollment utterances to guarantee low error rate during verification. However, in practical speaker verification, it is common to model speakers based on a limited amount of enrollment data, which will make the speaker models unreliable. This paper proposes four new adaptation methods for creating high-level speaker models to alleviate this undesirable effect. Unlike conventional methods in which only the phoneme-dependent background model is adapted, the proposed adaptation methods also adapts the phoneme-independent speaker model to fully utilize all the information available in the training data. A proportional factor, which is derived from the ratio between the phoneme-dependent background model and the phoneme-independent background model, is used to adjust the phoneme-independent speaker models during adaptation. The proposed method was evaluated under the NIST 2000 and NIST 2002 SRE frameworks. Experimental results show that the proposed adaptation method can alleviate the data-sparseness problem effectively and achieves a better performance when compared with traditional MAP adaptation.