Abstract
Speaker recognition over a public telephone network involves various types of transmission channels and handsets, which leads to mismatched channels (between the enrolled models and the test utterances), and hence to a significant decline in the speaker recognition performance. In this paper a cohort-based speaker model synthesis algorithm, which aims at synthesizing speaker models for channels where no enrollment data is available is proposed. This algorithm applies a priori knowledge of channels extracted from speaker-specific cohort sets to synthesize speaker models. Results for the China Criminal Police College (CCPC) speaker recognition corpus, which contains utterances from both a landline and a mobile channel, show significant improvements over the HT-Norm and UBM-based speaker model synthesis algorithms.
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