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.
@article{arxiv.2505.19671,
title = {Automated evaluation of children's speech fluency for low-resource languages},
author = {Bowen Zhang and Nur Afiqah Abdul Latiff and Justin Kan and Rong Tong and Donny Soh and Xiaoxiao Miao and Ian McLoughlin},
journal= {arXiv preprint arXiv:2505.19671},
year = {2025}
}