ISCA Archive Interspeech 2022
ISCA Archive Interspeech 2022

Design Guidelines for Inclusive Speaker Verification Evaluation Datasets

Wiebke Toussaint, Lauriane Gorce, Aaron Yi Ding

Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and reliable performance across speakers irrespective of their demographic, social and economic attributes. Current SV evaluation practices are insufficient for evaluating bias: they are over-simplified and aggregate users, not representative of usage scenarios encountered in deployment, and consequences of errors are not accounted for. This paper proposes design guidelines for constructing SV evaluation datasets that address these short-comings. We propose a schema for grading the difficulty of utterance pairs, and present an algorithm for generating inclusive SV datasets. We empirically validate our proposed method in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the count of utterance pairs/speaker, and the difficulty grading of utterance pairs have a significant effect on evaluation performance and variability. Our work contributes to the development of SV evaluation practices that are inclusive and fair.

doi: 10.21437/Interspeech.2022-10799

Cite as: Toussaint, W., Gorce, L., Ding, A.Y. (2022) Design Guidelines for Inclusive Speaker Verification Evaluation Datasets. Proc. Interspeech 2022, 1293-1297, doi: 10.21437/Interspeech.2022-10799

  author={Wiebke Toussaint and Lauriane Gorce and Aaron Yi Ding},
  title={{Design Guidelines for Inclusive Speaker Verification Evaluation Datasets}},
  booktitle={Proc. Interspeech 2022},