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

This paper proposes an automated system to assess children's speech fluency in low-resource languages (Tamil and Malay) by combining a fine-tuned multilingual ASR model with GPT-based classification of objective fluency metrics (phonetic/word error rates, speech rate, pause patterns). The system is designed for mother tongue language learning in Singapore primary schools, addressing the challenge of automated oral assessment in educational contexts.

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

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