Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of Wolof

By Elodie Gauthier, Laurent Besacier, Sylvie Voisin, Michael Melese, Uriel Pascal Elingui. 2016

85 citations recorded by the source corpus.

This article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our github repository.

Subfields: Artificial Intelligence, Natural Language Processing, Speech & Audio, Signal Processing

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Citations in the corpus

Learning Robust and Multilingual Speech Representations (2020, neutral): “We also train ASR models on 25 languages, including low-resource languages (e.g. Amharic, Fongbe, Swahili, Wolof), and show that our representations signiﬁcantly outperform both standard features and those pretrained only on clean English data in the language transfer setup.”
The zero resource speech challenge 2017 (2017, background): “The second surprise language is Wolof, and is the corpus described in [17].”
Towards Achieving Robust Universal Neural Vocoding (2018, background): “background noise [30], reverberation [31], or both [32]), iii) unseen languages (Ahmaric, Swahili and Wolof) recorded in sub-optimal recording situations [33] (i.”
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units (2020, unknown): “The Wolof corpus is described in [20].”
Self-Supervised Speech Representation Learning: A Review (2022, background): “For example, to demonstrate the robustness against unseen languages, the systems were developed with English, French, and Mandarin and tested on two “surprise” languages: German and Wolof.”