SVM modeling of ‘‘SNERF-Grams’’ for speaker recognition

ELIZABETH SHRIBERG; LUCIANA FERRER; ANAND VENKATARAMAN; SACHIN S. KAJAREKAR

Jeju

Congreso; Intl. Conf. Spoken Language Systems; 2004

ISCA

We describe a new approach to modeling idiosyncratic prosodic behavior for automatic speaker  recognition. The approach computes prosodic features by syllable (syllable based nonuniform extraction region features, or “SNERFs”), 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 system, as well as with both prosodic and word-based noncepstral systems.