Bias in Automated Speaker Recognition

Abstract

Automated speaker recognition uses data processing to identify speakers by their voice. Today, automated speaker recognition is deployed on billions of smart devices and in services such as call centres. Despite their wide-scale deployment and known sources of bias in related domains like face recognition and natural language processing, bias in automated speaker recognition has not been studied systematically. We present an in-depth empirical and analytical study of bias in the machine learning development workflow of speaker verification, a voice biometric and core task in automated speaker recognition. Drawing on an established framework for understanding sources of harm in machine learning, we show that bias exists at every development stage in the well-known VoxCeleb Speaker Recognition Challenge, including data generation, model building, and implementation. Most affected are female speakers and non-US nationalities, who experience significant performance degradation. Leveraging the insights from our findings, we make practical recommendations for mitigating bias in automated speaker recognition, and outline future research directions.

References in corpus (3)

• Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020
• Diversity and Inclusion Metrics in Subset Selection
• About Face: A Survey of Facial Recognition Evaluation

Cited by in corpus (5)

• Forensic deepfake audio detection using segmental speech features
• Uncovering Bias in Personal Informatics
• Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intents
• Study on the Fairness of Speaker Verification Systems on Underrepresented Accents in English
• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models