Findings of the Second AmericasNLP Competition on Speech-to-Text Translation

Indigenous languages, including those from the Americas, have received very little attention from the machine learning (ML) and natural language processing (NLP) communities. To tackle the resulting lack of systems for these languages and the accompanying social inequalities affecting their speakers, we conduct the second AmericasNLP competition (and the first one in collaboration with NeurIPS), which is centered around speech-to-text translation systems for Indigenous languages of the Americas. The competition features three tasks – (1) automatic speech recognition, (2) text-based machine translation, and (3) speech-to-text translation – and two tracks: constrained and unconstrained. Five Indigenous languages are covered: Bribri, Guarani, Kotiria, Wa’ikhana, and Quechua. In this overview paper, we describe the tasks, tracks, and languages, introduce the baseline and participating systems, and end with a summary of ongoing and future challenges for the automatic translation of Indigenous languages.