Speaker Group Encoding in Self-supervised Speech Recognition Models
A new research paper explores how self-supervised speech recognition models encode information about speaker groups. The study found that these models can identify characteristics such as gender, age, dialect, ethnicity, and native speaker status. Fine-tuning the models for speaker identification or automatic speech recognition alters the type of speaker group information retained, with ASR fine-tuning discarding phonetic variations while keeping semantic ones. The research suggests these findings could aid in developing fairer ASR algorithms. AI
IMPACT Findings could lead to more equitable ASR systems by understanding how models encode sensitive demographic data.