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This study fine-tunes the Whisper model for Automatic Speech Recognition (ASR) in Baniwa, an indigenous Arawakan language, using a corpus of 1,373 transcribed recordings. The results show that the adapted model achieves a Word Error Rate (WER) of 37.5% and a Character Error Rate (CER) of 7.45%, indicating the potential of multilingual models to serve low-resource languages. This work lays the groundwork for future advancements in ASR technologies for indigenous languages, which are often overlooked in the field.
Whisper's adaptation for Baniwa ASR reveals that multilingual models can effectively bridge the gap for low-resource languages, achieving competitive error rates.
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.
