ASR advancements for indigenous languages: Quechua, Guarani, Bribri,
  Kotiria, and Wa'ikhana
Monica Romero Sandra Gomez Iván G. Torre
Indigenous languages are a fundamental legacy in the development of human
communication, embodying the unique identity and culture of local communities
of America. The Second AmericasNLP Competition Track 1 of NeurIPS 2022 proposed
developing automatic speech recognition (ASR) systems for five indigenous
languages: Quechua, Guarani, Bribri, Kotiria, and Wa'ikhana. In this paper, we
propose a reliable ASR model for each target language by crawling speech
corpora spanning diverse sources and applying data augmentation methods that
resulted in the winning approach in this competition. To achieve this, we
systematically investigated the impact of different hyperparameters by a
Bayesian search on the performance of the language models, specifically
focusing on the variants of the Wav2vec2.0 XLS-R model: 300M and 1B parameters.
Moreover, we performed a global sensitivity analysis to assess the contribution
of various hyperparametric configurations to the performances of our best
models. Importantly, our results show that freeze fine-tuning updates and
dropout rate are more vital parameters than the total number of epochs of lr.
Additionally, we liberate our best models -- with no other ASR model reported
until now for two Wa'ikhana and Kotiria -- and the many experiments performed
to pave the way to other researchers to continue improving ASR in minority
languages. This insight opens up interesting avenues for future work, allowing
for the advancement of ASR techniques in the preservation of minority
indigenous and acknowledging the complexities involved in this important
endeavour.