Current state-of-the-art automatic speech recognition (ASR) systems recognize typical speech (very) well. However, recent research has shown that their performance degrades for "diverse" speech, i.e., speech that diverges from "typical" speech due to, among others, demographic and sociolinguistic factors. In this work, given the rapid development of ASR technologies, we examined the performance of nine recently released ASR systems developed by Google, Microsoft, Meta, NVIDIA, and OpenAI, and three custom ASR models trained from scratch, on Dutch diverse speech. Our results showed that although overall recognition results differ quite substantially between the different systems, all systems show similar patterns regarding recognition performance for diverse speaker groups: for most ASR systems and models, language proficiency differences and severe speech motor impairment had a greater impact on performance disparities between speaker groups than demographic or sociolinguistic factors, indicating that acoustic variability due to demographic and sociolinguistic factors is well-represented in "typical speech" training data and consequently is well-modeled in the models. Furthermore, we found that differences in data processing pipelines and decoding setups significantly influenced recognition performance. Importantly, updates to company-developed ASR systems do not always improve performance of or reduce performance disparities between diverse speaker groups.
Zhang et al. (Wed,) studied this question.