Causal analysis of ASR errors for children: Quantifying the impact of physiological, cognitive, and extrinsic factors

The increasing use of children’s automatic speech recognition (ASR) systems has spurred research efforts to improve the accuracy of models designed for children’s speech in recent years. The current approach utilizes either open-source speech foundation models (SFMs) directly or fine-tuning them with children’s speech data. These SFMs, whether open-source or fine-tuned for children, often exhibit higher word error rates (WERs) compared to adult speech. However, there is a lack of systemic analysis of the cause of this degraded performance of SFMs. Understanding and addressing the reasons behind this performance disparity is crucial for improving the accuracy of SFMs for children’s speech. Our study addresses this gap by investigating the causes of accuracy degradation and the primary contributors to WER in children’s speech. In the first part of the study, we conduct a comprehensive benchmarking study on two self-supervised SFMs ( Wav2Vec2.0 and Hubert ) and two weakly supervised SFMs ( Whisper and Massively Multilingual Speech (MMS) ) across various age groups on two children speech corpora, establishing the raw data for the causal inference analysis in the second part. In the second part of the study, we analyze the impact of physiological factors (age, gender), cognitive factors (pronunciation ability), and external factors (vocabulary difficulty, background noise, and word count) on SFM accuracy in children’s speech using causal inference. The results indicate that physiology (age) and particular external factor (number of words in audio) have the highest impact on accuracy, followed by background noise and pronunciation ability. Fine-tuning SFMs on children’s speech reduces sensitivity to physiological and cognitive factors, while sensitivity to the number of words in audio persists. • A causal framework to analyze factors contributing to ASR errors for children. • We include physiological, cognitive, and extrinsic factors in our analysis. • Two self-supervised ( Wav2Vec2.0 and Hubert ) and two weakly supervised ( Whisper and Massively Multilingual Speech (MMS) ) SFMs in analysis. • Insights into fine-tuning of speech foundation models.