  Automatic speech recognition (ASR) systems promise to deliver objective
interpretation of human speech. Practice and recent evidence suggests that the
state-of-the-art (SotA) ASRs struggle with the large variation in speech due to
e.g., gender, age, speech impairment, race, and accents. Many factors can cause
the bias of an ASR system. Our overarching goal is to uncover bias in ASR
systems to work towards proactive bias mitigation in ASR. This paper is a first
step towards this goal and systematically quantifies the bias of a Dutch SotA
ASR system against gender, age, regional accents and non-native accents. Word
error rates are compared, and an in-depth phoneme-level error analysis is
conducted to understand where bias is occurring. We primarily focus on bias due
to articulation differences in the dataset. Based on our findings, we suggest
bias mitigation strategies for ASR development.
