Parameterization of Prosodic Feature Distributions for SVM Modeling in Speaker Recognition

LUCIANA FERRER; ELIZABETH SHRIBERG; SACHIN S. KAJAREKAR; KEMAL SÖNMEZ

Honolulu

Congreso; IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2007

IEEE

Multiple recent studies have shown that speaker recognition performance  using frame-based cepstral features is improved by adding higher-level information, including prosodic and lexical features. This paper explores the important question of finding a good kernel for a system that models syllable-based prosodic features using support vector machines (SVMs). The system has been the best performing of our high-level systems in the last two NIST evaluations, and gives significant improvements when combined with cepstral based systems. We introduce two new methods for transforming the syllable-level features into a single high-dimensional vector that can be well modeled by SVMs, resulting in significant gains in speaker recognition performance.