An End-to-End Approach for Child Reading Assessment in the Xhosa Language

TL;DR

This paper introduces a new dataset of child Xhosa speech and evaluates end-to-end speech recognition models, highlighting data quantity and class balancing as key factors for improving performance in low-resource language settings.

Contribution

It provides the first child speech dataset in Xhosa and assesses state-of-the-art models, demonstrating how data quantity and class balancing affect recognition accuracy.

Findings

Model performance improves with more training data.

Training on multiple classes enhances wav2vec 2.0 accuracy.

Data collection strategies are crucial for low-resource languages.

Abstract

Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure the effectiveness of these programs and AI can be a reliable and economical tool to support educators with this task. Developing accurate automatic reading assessment systems for child speech in low-resource languages poses significant challenges due to limited data and the unique acoustic properties of children's voices. This study focuses on Xhosa, a language spoken in South Africa, to advance child speech recognition capabilities. We present a novel dataset composed of child speech samples in Xhosa. The dataset is available upon request and contains ten words and letters, which are part of the Early Grade Reading Assessment (EGRA) system. Each recording is labeled with an online and cost-effective approach by multiple markers and a subsample is validated by an independent EGRA reviewer. This dataset is evaluated with three fine-tuned state-of-the-art end-to-end models: wav2vec 2.0, HuBERT, and Whisper. The results indicate that the performance of these models can be significantly influenced by the amount and balancing of the available training data, which is fundamental for cost-effective large dataset collection. Furthermore, our experiments indicate that the wav2vec 2.0 performance is improved by training on multiple classes at a time, even when the number of available samples is constrained.