Bias in Automated Speaker Recognition
This addresses bias in speaker recognition systems, which are widely deployed in smart devices and call centers, and is incremental as it applies an existing bias framework to a new domain.
The paper systematically studied bias in automated speaker recognition, finding that bias exists at every stage of the machine learning workflow, with female speakers and non-US nationalities experiencing significant performance degradation.
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.