Towards Measuring and Scoring Speaker Diarization Fairness
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Abstract (arXiv)
Speaker diarization, or the task of finding "who spoke and when", is now used in almost every speech processing application. Nevertheless, its fairness has not yet been evaluated because there was no protocol to study its biases one by one. In this paper we propose a protocol and a scoring method designed to evaluate speaker diarization fairness. This protocol is applied on a large dataset of spoken utterances and report the performances of speaker diarization depending on the gender, the age, the accent of the speaker and the length of the spoken sentence. Some biases induced by the gender, or the accent of the speaker were identified when we applied a state-of-the-art speaker diarization method.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 20 Feb 2023.
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References · 10
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 732
- 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 124
- 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
- 2021End-to-end speaker segmentation for overlap-aware resegmentationcited by 80
- 2020Spot the conversation: speaker diarisation in the wildcited by 79
- 2020Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenariocited by 68
- 2019Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detectioncited by 27
- 2021Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractorscited by 15
- 2021AequeVox: Automated Fairness Testing of Speech Recognition Systemscited by 9
- 2020Multi-class Spectral Clustering with Overlaps for Speaker Diarizationcited by 8
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