A phonetic basis of accent bias in speaker identification technology

Speaker identification (SID) technology aims to determine whether the speech in a recording of an unknown speaker matches that of speaker known to the SID system. If the unknown speaker comes from a community that is underrepresented in the SID training data (e.g., accent), the SID model is more likely to confuse the unknown speaker for other speakers in that speaker community: group-level characteristics are mistaken for individual identifiers (all speakers with that accent seem like the same speaker to SID). Previous solutions include reweighting training data to create balance across speaker groups. While effective, such solutions are insufficient because they require foreknowledge of all potential speaker groups to be tested. The present study seeks a solution by first understanding how group-level characteristics are represented in SID models, focusing on dialect diversity within first-language English speakers. We hypothesize that filter properties (e.g. vowel formants) are more heavily represented in SID models than other uniquely-identifying spectral components. Preliminary results show that speaker clusters based on vowel measures alone are dissimilar to clusters formed by the SID model representations (embeddings) extracted from the same recordings—except for certain underrepresented accents (African American Language), suggesting SID representations of speech differ by speaker group.