Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/4335| Title: | A LIGHTWEIGHT DOWNSCALED APPROACH TO AUTOMATIC SPEECH RECOGNITION FOR SMALL INDIGENOUS LANGUAGES |
| Authors: | Stan, G. V. Baart, A. Dittoh, F. Akkermans, H. Bon, A. |
| Keywords: | Under-resourced/indigenous languages Low resource environments Machine learning Voice-based technologies Neural networks Auto matic speech recognitio |
| Issue Date: | 2022 |
| Publisher: | Association for Computing Machinery |
| Abstract: | Development of fully featured Automatic Speech Recognition (ASR) systems for a complete language vocabulary generally requires large data repositories, massive computing power, and a stable digital network infrastructure. These conditions are not met in the case of many indigenous languages. Based on our research for over a decade in West Africa, we present a lightweight and downscaled approach to AI-based ASR and describe a set of associated experiments. The aim is to produce a variety of limited-vocabulary ASRs as a basis for the development of practically useful (mobile and radio) voice-based information services that fit needs, preferences and knowledge of local rural communities. |
| Description: | Proceedings of the 14th ACM Web Science Conference 2022. Barcelona, Spain. |
| URI: | http://hdl.handle.net/123456789/4335 |
| Appears in Collections: | Conference Proceedings |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| A LIGHTWEIGHT DOWNSCALED APPROACH TO AUTOMATIC SPEECH RECOGNITION FOR SMALL INDIGENOUS LANGUAGES.pdf | 1.33 MB | Adobe PDF | View/Open |
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