Fine-Tuning Whisper for Automatic Speech Recognition in Baniwa: A Preliminary Study
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Computer Science > Computation and Language
arXiv:2608.26060 (cs)
[Submitted on 26 Aug 2026]
Title:Fine-Tuning Whisper for Automatic Speech Recognition in Baniwa: A Preliminary Study
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Abstract:Automatic Speech Recognition (ASR) technologies have achieved remarkable performance in recent years through the use of large multilingual foundation models. However, most advances remain concentrated on high-resource languages, while indigenous languages continue to suffer from a lack of speech resources and language technologies. This work presents a preliminary study on the adaptation of Whisper for Automatic Speech Recognition in Baniwa, an indigenous Arawakan language spoken in Brazil, Colombia, and Venezuela. The experiments were conducted using a corpus of 1,373 manually transcribed recordings obtained from a linguistic documentation project. The corpus contains approximately 0.54 hours of speech and consists primarily of isolated words and short elicited utterances. The Whisper Small model was fine-tuned using supervised learning and evaluated using Word Error Rate (WER) and Character Error Rate (CER). The best model achieved a WER of 37.5% and a CER of 7.45%, demonstrating that multilingual foundation models can be successfully adapted to extremely low-resource indigenous languages. The results establish an initial baseline for Baniwa Automatic Speech Recognition and provide a foundation for future research involving larger datasets, language-specific adaptation strategies, and post-processing techniques.
| Comments: | 12 pages, 3 tables. Preliminary study |
| Subjects: | Computation and Language (cs.CL); Machine Learning (stat.ML) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2608.26060 [cs.CL] |
| (or arXiv:2608.26060v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26060
arXiv-issued DOI via DataCite (pending registration)
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