Unseen handset mismatch compensation based on feature/model-space a priori knowledge interpolation for robust speaker recognition

Unseen but mismatch handset is the major source of performance degradation for speaker recognition in telecommunication environment. In this paper, an unseen handset characteristics estimation method based on a priori knowledge interpolation (AKI) is proposed. AKI could be applied in both the feature and model space to interpolate the feature and model transformation functions measured using stochastic matching (SM) and maximum likelihood linear regression (MLLR), respectively. Cross-validation experimental results on HTIMIT database showed that the average speaker recognition rate could be improved from 59.60/./57.8% to 73.8%/66.8% for seen/unseen handsets. It is therefore a promising method for robust speaker recognition.