Inclusive Speaker Verification with Adaptive thresholding
This addresses inclusivity in commercial speaker verification systems by mitigating performance disparities for different user groups, though it is incremental as it builds on existing thresholding methods.
The paper tackles the problem of speaker verification systems performing differently across gender and age groups, proposing an adaptive thresholding framework that reduces the False Rejection Rate for specific groups while maintaining a desired False Acceptance Rate, with experiments showing reductions on datasets like VoxCeleb1 and OGI Kids' Speech Corpus.
While using a speaker verification (SV) based system in a commercial application, it is important that customers have an inclusive experience irrespective of their gender, age, or ethnicity. In this paper, we analyze the impact of gender and age on SV and find that for a desired common False Acceptance Rate (FAR) across different gender and age groups, the False Rejection Rate (FRR) is different for different gender and age groups. To optimize FRR for all users for a desired FAR, we propose a context (e.g. gender, age) adaptive thresholding framework for SV. The context can be available as prior information for many practical applications. We also propose a concatenated gender/age detection model to algorithmically derive the context in absence of such prior information. We experimentally show that our context-adaptive thresholding method is effective in building a more efficient inclusive SV system. Specifically, we show that we can reduce FRR for specific gender for a desired FAR on the voxceleb1 test set by using gender-specific thresholds. Similar analysis on OGI kids' speech corpus shows that by using an age-specific threshold, we can significantly reduce FRR for certain age groups for desired FAR.