# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
###### Abstract  
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 Competition Track 1 of NeurIPS 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 Wav2 vec2.0 XLS-R 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, [PERSON]' 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. automatic speech recognition; natural language processing; low-resource languages; Indigenous languages; NeurIPS +
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available for a few dozen languages [6]. Preserving Indigenous languages is an objective that safeguards local communities' heritage and cultural identity and is crucial for retaining critical knowledge associated with environmental interaction and understanding the social aspects of language evolution [7]. To address the gap between AI solutions for majority and Indigenous languages, the initiative Second AmericasNLP Competition Track 1 of NeurIPS 2022 [8] proposed that the participants produce ASR systems for five different Indigenous languages: Quechua, Guarani, Bribri, Kotiria, and Wa'ikhana. Bribri is a language of the Chibchan family, spoken by approximately 7500 speakers in areas of Costa Rica and Panama [9; 10; 11]. Meanwhile, Guarani is spoken in Paraguay, Argentina, Bolivia, and Brazil and is one of the most significant Amerindian languages in terms of numerical importance, with 6-10 million speakers [11; 12; 13]. Kotiria--or Wanano--is one of the sixteen languages of the eastern branch of the Tukanoan language family [14], and it is spoken in the northwest of the Amazon region, in the territory between Brazil and Colombia. Approximately 2000 speakers are distributed between Colombia and Brazil [15; 16]. Wa'ikhana--or Piratapuyo--is also an eastern Tukanoan language spoken in Brazil and Colombia, with less than 2000 speakers [15; 17; 18]. Finally, Quechua is a heterogeneous language spoken across the Andes, stretching from southern Colombia to northwest Argentina, encompassing a diverse family of languages and dialects [19], with more than 6.5 million speakers [20]. Indigenous languages and, more generally, languages with limited or relatively fewer data available are denoted in AI and NLP as low-resource languages with a critical level of under-documentation [6]. They pose a significant challenge for developing robust and accurate ASR systems, as actual algorithms primarily rely on the availability of substantial amounts of labeled data.