PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification

ABSTRACT Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's content. The lexical content L determines the phonetic structure a measure of the frequency or duration of phonemes, as a P, which in turn determines the acoustics A. Thus, any production crucial cue in speaker verification. A novel Phoneme-Debiasing Attention of a signal actually represents the draws of all three variables. Framework (PDAF) is introduced, integrating with existing Content-agnostic verification systems, however, only consider the attention frameworks to mitigate biases caused by phonetic dominance. This approach paves the way for more accurate and reliable identity authentication through voice.