Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
Chunxi Liu, Michael Picheny, Leda Sari, Pooja Chitkara, Alex Xiao, Xiaohui Zhang, Mark Chou, Andres Alvarado
Abstract
The problem of machine learning systems demonstrating bias towards specific groups of individuals has been studied extensively, particularly in the Facial Recognition area, but much less so in Automatic Speech Recognition (ASR). This paper presents initial Speech Recognition results on “Casual Conversations” – a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone. The entire corpus has been manually transcribed, allowing for detailed ASR evaluations across these metadata. Multiple ASR models are evaluated, including models trained on LibriSpeech, 14,000 hour transcribed, and over 2 million hour untranscribed social media videos. Significant differences in word error rate across gender and skin tone are observed at times for all models. We are releasing human transcripts from the Casual Conversations dataset to encourage the community to develop a variety of techniques to reduce these statistical biases.
BibTeX
@inproceedings{icassp2022_towardsmeasuring,
title = {Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions},
author = {Chunxi Liu and Michael Picheny and Leda Sari and Pooja Chitkara and Alex Xiao and Xiaohui Zhang and Mark Chou and Andres Alvarado and Caner Hazirbas and Yatharth Saraf},
booktitle = {ICASSP 2022},
year = {2022}
}