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

arXiv: https://arxiv.org/abs/2505.19671 · HTML: https://mlsanity.com/paper/2505.19671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 11 of 11)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [Sparsely Shared LoRA on Whisper for Child Speech Recognition](https://mlsanity.com/paper/2309.11756/) (2023)
- [ASR-Free Pronunciation Assessment](https://mlsanity.com/paper/2005.11902/) (2020)
- [A Study on Incorporating Whisper for Robust Speech Assessment](https://mlsanity.com/paper/2309.12766/) (2023)
- [Data Augmentation For Children's Speech Recognition -- The "Ethiopian" System For The SLT 2021 Children Speech Recognition Challenge](https://mlsanity.com/paper/2011.04547/) (2020)
- [MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios](https://mlsanity.com/paper/2308.12490/) (2023)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)

## Built on by (most cited first, 1 of 1)

- [CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation](https://mlsanity.com/paper/2511.11104/) (2025)

## Abstract (arXiv)

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.