Abstract
In telephone-based speaker identification, variation in handset characteristics can introduce severe speech variabilityeven 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 are 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 are 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 distirted vectors. Experimental results based on 138 speakers of the YOHO and telephone YOHO corppora show that the proposed approach is computationally efficient and is able to increase the accuracy from 17% (without compensation) to 85% (with compensation).
S. Y. Kung is on sabbatical from the Princeton, University, Usa. He is currently a distinguished chair professor of the Department of Electronic and Information Engineering, The HOng Kong Polytechnic University. This project was supported by the Hong Kong Polytechnic University Grant No. 1.42.37.A410.
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Yiu, K.K., Mak, M.W., Kung, S.Y. (2001). A GMM-Based Handset Selector for Channel Mismatch Compensation with Aplications to Speaker Identification. In: Shum, HY., Liao, M., Chang, SF. (eds) Advances in Multimedia Information Processing — PCM 2001. PCM 2001. Lecture Notes in Computer Science, vol 2195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45453-5_156
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DOI: https://doi.org/10.1007/3-540-45453-5_156
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