Abstract
We describe a new approach to modeling idiosyncratic prosodic behavior for automatic speaker recognition . The approach computes prosodic features by syllable (sy llablebased nonuniform extraction region features, or “SN ERFs”), and models the syllable-feature sequences (“SNERF-grams”) using support vector machines (SVMs). We evaluate performance on development data for a system submitted to the NIST 2004 Speaker Recognition Evaluation. Results show that SNERF-grams provide significant performance gains when combined with a state-of-the-art baseline syst em, as well as with both prosodic and word-based noncepstral systems.
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