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arXiv · 2505.19671

Automated evaluation of children's speech fluency for low-resource languages

Bowen Zhang·Nur Afiqah Abdul Latiff·Justin Kan·Rong Tong·Donny Soh·Xiaoxiao Miao·Ian McLoughlin
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Abstract

Assessment of children's speaking fluency in education is well researched for majority languages, but remains highly challenging for low resource languages. This paper proposes a system to automatically assess fluency by combining a fine-tuned multilingual ASR model, an objective metrics extraction stage, and a generative pre-trained transformer (GPT) network. The objective metrics include phonetic and word error rates, speech rate, and speech-pause duration ratio. These are interpreted by a GPT-based classifier guided by a small set of human-evaluated ground truth examples, to score fluency. We evaluate the proposed system on a dataset of children's speech in two low-resource languages, Tamil and Malay and compare the classification performance against Random Forest and XGBoost, as well as using ChatGPT-4o to predict fluency directly from speech input. Results demonstrate that the proposed approach achieves significantly higher accuracy than multimodal GPT or other methods.

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Bowen Zhang, Nur Afiqah Abdul Latiff, Justin Kan, Rong Tong, Donny Soh, Xiaoxiao Miao, Ian McLoughlin. 2025-05-26. Automated evaluation of children's speech fluency for low-resource languages. https://doi.org/10.21437/interspeech.2025-1358

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