Linguistic sources of performance disparities in Dutch Automatic Speech Recognition for non-native adults

Abstract

Automatic Speech Recognition (ASR) systems are increasingly used in real-world applications, but their performance is not equally reliable across speaker groups. Previous work has shown state-of-the-art ASR systems to perform worse for non-native speakers; the linguistic factors that contribute to this, however, are yet to be understood. To address this gap, this study investigates how utterance length and word category affect ASR performance for Dutch speech from non-native adult speakers. In addition, it investigates how these effects differ between read speech and human-machine interaction (HMI) speech. The study uses ASR outputs from Google Chirp 2 and Whisper large-v3 on non-native adult speech from the JASMIN corpus. The analysis examines how error patterns relate to utterance length and part-of-speech category, with insertion, deletion, and substitution errors analyzed separately for each factor. The results show that ASR performance varies systematically with both factors: very short utterances are especially error-prone, length effects differ between read and HMI speech, and word-category patterns are most visible in substitution and insertion errors. These findings highlight the need for more fine-grained ASR evaluation methods that account for how linguistic context influences recognition errors in non-native speech.