Automatic Speech Recognition Advancements for Indigenous Languages of the Americas

Indigenous languages are a fundamental legacy in the development of human communication, embodying the unique identity and culture of local communities in America. The Second AmericasNLP (Americas Natural Language Processing) Competition Track 1 of NeurIPS (Neural Information Processing Systems) 2022 proposed the task of training automatic speech recognition (ASR) systems for five Indigenous languages: Quechua, Guarani, Bribri, Kotiria, and Wa'ikhana. In this paper, we describe the fine-tuning of a state-of-the-art ASR model for each target language, using approximately 36.65 h of transcribed speech data from diverse sources enriched with data augmentation methods. We systematically investigate, using a Bayesian search, the impact of the different hyperparameters on the Wav2vec2.0 XLS-R (Cross-Lingual Speech Representations) variants of 300 M and 1 B parameters. Our findings indicate that data and detailed hyperparameter tuning significantly affect ASR accuracy, but language complexity determines the final result. The Quechua model achieved the lowest character error rate (CER) (12.14), while the Kotiria model, despite having the most extensive dataset during the fine-tuning phase, showed the highest CER (36.59). Conversely, with the smallest dataset, the Guarani model achieved a CER of 15.59, while Bribri and Wa'ikhana obtained, respectively, CERs of 34.70 and 35.23. Additionally, Sobol' sensitivity analysis highlighted the crucial roles of freeze fine-tuning updates and dropout rates. We release our best models for each language, marking the first open ASR models for Wa'ikhana and Kotiria. This work opens avenues for future research to advance ASR techniques in preserving minority Indigenous languages