The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
Ajinkya Kulkarni Anna Tokareva Rameez Qureshi Miguel Couceiro
In the field of spoken language understanding, systems like Whisper and
Multilingual Massive Speech (MMS) have shown state-of-the-art performances.
This study is dedicated to a comprehensive exploration of the Whisper and MMS
systems, with a focus on assessing biases in automatic speech recognition (ASR)
inherent to casual conversation speech specific to the Portuguese language. Our
investigation encompasses various categories, including gender, age, skin tone
color, and geo-location. Alongside traditional ASR evaluation metrics such as
Word Error Rate (WER), we have incorporated p-value statistical significance
for gender bias analysis. Furthermore, we extensively examine the impact of
data distribution and empirically show that oversampling techniques alleviate
such stereotypical biases. This research represents a pioneering effort in
quantifying biases in the Portuguese language context through the application
of MMS and Whisper, contributing to a better understanding of ASR systems'
performance in multilingual settings.