Soft-decision a Priori Knowledge Interpolation for Robust Telephone Speaker Identification

Handsets which are not seen in the training phase (a.k.a unseen handsets) are main sources of performance degradation for speaker identification (SID) applications in telecommunication environments. To alleviate the problem, a soft-decision a priori knowledge interpolation (SD-AKI) method of handset characteristic estimation for handset mismatch-compensated SID is proposed in this paper. The idea of the SD-AKI method is to first collect a set of characteristics of seen handsets in the training phase, and to then estimate the characteristic of the unknown testing handset by interpolating the set of seen handset characteristics in the test phase. The estimated handset characteristic is then used to compensate for handset mismatch for robust SID. The SD-AKI method can be realized in both feature and model spaces. Experimental results on the handset TIMIT (HTIMIT) database showed that both the proposed featureand model-space SD-AKI schemes were more robust than the blind cepstral mean subtraction (CMS), feature warping (FW) methods and their hard-decision counterpart (HD-AKI) for both cases of all-handset and unseen-handset SID tests. It is therefore a promising robust SID method.