PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification

TL;DR

This paper introduces PDAF, a novel attention framework that reduces phonetic bias in speaker verification systems, leading to more accurate voice-based identity authentication by adjusting phoneme weighting.

Contribution

The paper presents PDAF, a new phonetic debiasing attention framework that integrates with existing models to improve speaker verification accuracy by mitigating phonetic dominance biases.

Findings

PDAF improves speaker verification accuracy across multiple datasets.

Different weighting strategies influence phonetic feature importance.

PDAF enhances robustness against phonetic content biases.

Abstract

Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's content. However, this paper challenges this by highlighting the importance of phonetic dominance, a measure of the frequency or duration of phonemes, as a crucial cue in speaker verification. A novel Phoneme Debiasing Attention Framework (PDAF) is introduced, integrating with existing attention frameworks to mitigate biases caused by phonetic dominance. PDAF adjusts the weighting for each phoneme and influences feature extraction, allowing for a more nuanced analysis of speech. This approach paves the way for more accurate and reliable identity authentication through voice. Furthermore, by employing various weighting strategies, we evaluate the influence of phonetic features on the efficacy of the speaker verification system.