Integrating Base Performance and Performance Differences in Automatic Speech Recognition Metrics
B.V. van Vliet (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Odette Scharenborg – Mentor (TU Delft - Multimedia Computing)
Jorge Martinez – Mentor (TU Delft - Multimedia Computing)
N.M. Gürel – Coach (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Automatic Speech Recognition (ASR) systems are becoming increasingly popular in this day and age. Unfortunately, due to inherent biases within these systems, performance disparities exist among specific demographic groups. Bias metrics can be used to measure this bias. Within ASR they represent a niche area that has not yet been thoroughly explored. The few bias metrics that exist in literature mainly centre around the performance differences between speaker groups. This paper proposes two new bias metrics that focus not only on performance differences, but also take the base performance into account: Weighted Performance Bias (WPB) and Intergroup Weighted Performance Bias (IWPB). Although the lack of ground truth makes the results less easily interpretable, the results show similar trends within the new metrics as those defined in literature: bias is greatest among non-native Dutch speech.