Building an ASR Solution for Training and Assessing Children's Reading

2026-06-30

AI Summary

This study addresses the scarcity of automatic speech recognition (ASR) systems for African languages—such as Bambara—tailored to children’s read-aloud speech, which hinders reproducible literacy assessments. We present the first open-source ASR benchmark for Bambara child read-aloud data, encompassing field data collection, model adaptation, and classroom validation. Our proposed Soloni model, based on Fast-Conformer and adapted to Bambara phonetics, integrates TDT/CTC decoding with SpecAugment data augmentation, and is benchmarked against QuartzNet. Experimental results reveal architecture-dependent benefits from repeated read-aloud utterances; the optimized Soloni model reduces word error rate (WER) from 0.42 to 0.22 and character error rate (CER) from 0.15 to 0.08, substantially outperforming baseline systems. The model has been successfully deployed in ten classrooms.

Abstract

Automatic speech recognition for children's reading remains underdeveloped for most African languages, including Bambara, despite its potential value for reproducible literacy assessment. We present an open-source system for assessing children's reading in Bambara, developed through an end-to-end process linking field data collection, benchmark construction, model adaptation, a reading application, and classroom validation. A mobile collection and assessment app was used to collect 55 hours of raw reading speech from 60 children, from which we construct a public benchmark for Bambara child-reading assessment. Fine-tuning experiments compare Soloni, a Bambara-adapted Fast-Conformer ASR framework with TDT and CTC decoders, with QuartzNet, a compact convolutional ASR architecture. The best Soloni model reduces WER from 0.42 to 0.22 and CER from 0.15 to 0.08, substantially outperforming QuartzNet on the isolated benchmark. The experiments further show that repeated readings of the same texts provide architecture-dependent benefits: they substantially improve QuartzNet but add only marginal gains for Soloni, while SpecAugment regulates training without exceeding the best unaugmented configuration. Disaggregated analysis identifies children under 10 as the main source of residual errors, motivating targeted collection from younger readers. Ten classroom trials supported continued use of the application.

Problem

• Automatic Speech Recognition
• Children's Reading
• Bambara
• Literacy Assessment
• Low-resource Languages

Innovation

• child ASR
• Bambara
• Fast-Conformer
• reading assessment
• benchmark dataset