A GMM-based handset selector for channel mismatch compensation with applications to speaker identification

In telephone-based speaker identification, variation in handset characteristics can introduce severe speech variability even for speech uttered by the same speaker. This paper proposes a method to compensate the variation in handset characteristics, In the method, a number of Gaussian mixture models axe independently trained to identify the most likely handset given a test utterance. The identified handset is used to select a compensation vector from a set of pre-computed vectors, where the pre-computed vectors axe the average frame-by-frame differences between the clean and distorted utterances. The clean features are then recovered by subtracting the selected compensation vector from the distorted vectors. Experimental results based on 138 speakers of the YOHO and telephone YOHO corpora show that the proposed approach is computationally efficient and is able to increase the accuracy from 17% (without compensation) to 85% (with compensation).