Publication Type
Conference Proceeding
Publication Date (Issue Year)
2025
Journal Name
Paper-Datasets_and_Benchmarks_Track
Abstract
Intent classification models have made a significant progress in recent years. However, previous studies primarily focus on high-resource language datasets, which results in a gap for low-resource languages and for regions with high rates of illiteracy, where languages are more spoken than read or written. This is the case in Senegal, for example, where Wolof is spoken by around 90% of the population, while the national illiteracy rate remains at of 42%. Wolof is actually spoken by more than 10 million people in West African region. To address these limitations, we introduce the Wolof Banking Speech Intent Classification Dataset (WolBanking77), for academic research in intent classification. WolBanking77 currently contains 9,791 text sentences in the banking domain and more than 4 hours of spoken sentences. Experiments on various baselines are conducted in this work, including text and voice state-of-the-art models. The results are very promising on this current dataset. In addition, this paper presents an in-depth examination of the dataset’s contents. We report baseline F1-scores and word error rates metrics respectively on NLP and ASR models trained on WolBanking77 dataset and also comparisons between models. Dataset and code available at: wolbanking77.
Keywords
WolBanking77, Wolof Banking, Speech Intent Classification, Dataset
Rsif Scholar Name
Abdou Karim KANDJI
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
KANDJI, A. K., Precioso, F., Ba, C., Ndiaye, S., & Ndione, A. (2025). WolBanking77: Wolof Banking Speech Intent Classification Dataset. Paper-Datasets_and_Benchmarks_Track https://doi.org/10.52202/085713-4232