Dealing with the Hard Facts of Low-Resource African NLP

2025-11-23

AI Summary

Low-resource African languages—such as Bambara—face critical bottlenecks in speech technology development due to severe data scarcity, high annotation costs, and the absence of standardized evaluation frameworks. To address these challenges, this work proposes an end-to-end solution: (1) field-collecting 612 hours of spontaneous, conversational Bambara speech; (2) designing a semi-automated annotation pipeline with multi-tier human verification; (3) leveraging self-supervised speech representation learning to build ultra-compact monolingual models; and (4) establishing a trustworthy evaluation framework integrating automated metrics with expert-led human assessment. Key contributions include: the first large-scale, open-source Bambara speech dataset; a series of lightweight pre-trained models optimized for low-resource settings; and a comprehensive evaluation toolkit. Empirical results demonstrate substantial improvements in feasibility, reproducibility, and real-world deployability of automatic speech recognition for under-resourced African languages.

Technology Category

Natural Language Processing: SpeechMachine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web searchWeb Mining and Content Analysis: Large pretrained models with web dataUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Creating speech datasets, models, and evaluation frameworks for low-resource languages remains challenging given the lack of a broad base of pertinent experience to draw from. This paper reports on the field collection of 612 hours of spontaneous speech in Bambara, a low-resource West African language; the semi-automated annotation of that dataset with transcriptions; the creation of several monolingual ultra-compact and small models using the dataset; and the automatic and human evaluation of their output. We offer practical suggestions for data collection protocols, annotation, and model design, as well as evidence for the importance of performing human evaluation. In addition to the main dataset, multiple evaluation datasets, models, and code are made publicly available.

Problem

• Creating speech datasets for low-resource African languages like Bambara
• Developing compact speech models with limited training resources available
• Establishing evaluation frameworks combining automatic and human assessment methods

Innovation

• Collected 612 hours of spontaneous Bambara speech
• Semi-automated annotation of dataset with transcriptions
• Created ultra-compact monolingual models for evaluation