PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification
This addresses the issue of biased speaker verification for users relying on voice authentication, though it appears incremental as it integrates with existing attention frameworks.
The paper tackled the problem of speaker verification systems overlooking speech content by introducing a Phoneme Debiasing Attention Framework (PDAF) to mitigate biases from phonetic dominance, resulting in more accurate identity authentication through voice.
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.