Utterance Length and ASR Performance Disparities
Native and Non-Native Dutch-Speaking Children in Read and Human-Machine Interaction Speech
Z.D. Comvalius (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Y. Zhang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C.R.M.M. Oertel Genannt Bierbach – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
O.E. Scharenborg – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Automatic speech recognition (ASR) is increasingly used in speech-based services,
but its performance is often worse for children and non-native speakers. This study
examines whether utterance length helps explain the higher word error rates observed for non-native Dutch-speaking children compared with native Dutch-speaking children, and whether this relationship differs between read speech and human-machine interaction (HMI) speech. Using 42,415 utterances from 188 child speakers in the Dutch Jasmin corpus and ASR outputs from Google Chirp 2 and Whisper-large-v3, the analysis compares word error rates by utterance length, measures native/non-native performance gaps, standardizes both speaker groups to the same length distribution, and estimates uncertainty with speaker-level bootstrap resampling. The results show that shorter utterances have higher error rates for both groups, speech types, and systems. Utterance length explains a measurable part of the read-speech disparity, because the gap decreases with length and is reduced after length standardization. However, a substantial gap remains. For HMI speech, the evidence is less consistent. Overall, utterance length affects how ASR disparities are measured, but it does not fully explain the disadvantage for non-native child speech.