Recent Progress in Prosodic Speaker Verification

Marcel Kockmann, Luciana Ferrer, Lukas Burget, Elizabeth Shriberg, and Jan Cernocky

Abstract

We describe recent progress in the field of prosodic modeling for

speaker verification. In a previous paper, we proposed a technique

for modeling syllable-based prosodic features that uses a multinomial

subspace model for feature extraction and within-class covariance

normalization or linear discriminant analysis for session variability

compensation. In this paper, we show that performance can

be significantly improved with the use of probabilistic linear discriminant

analysis (PLDA) for session variability compensation. This

system does not require score normalization. We report an equal error

rate below 7% on a NIST 2008 task. To our knowledge, this is the

best reported result to date for a prosodic system for speaker recognition.

Fusion of this system with a state-of-the-art acoustic baseline

system yields 10% relative improvement in the new detection cost

function (DCF) as defined by NIST.

Details

Publication typeInproceedings
Pages4556-4559
PublisherIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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