Robust Speaker Verification with Principal Pitch Components

摘要

We are presenting a new method that improves the accuracy of text dependent speaker verification systems. The new method exploits a set of novel speech features derived from a principal component analysis of pitch synchronous voiced speech segments. We use the term principal pitch components (PPCs) or optimal pitch bases (OPBs) to denote the new feature set. Utterance distances computed from these new PPC features are only loosely correlated with utterance distances computed from cepstral features. A distance measure that combines both cepstral and PPC features provides a discriminative power that cannot be achieved with cepstral features alone. By augmenting the feature space of a cepstral baseline system with PPC features we achieve a significant reduction of the equal error probability of incorrect customer rejection versus incorrect impostor acceptance. The proposed method delivers robust performance in various noise conditions.