OpenASR20: An Open Challenge for Automatic Speech Recognition of Conversational Telephone Speech in Low-Resource Languages

In 2020, the National Institute of Standards and Technology (NIST), in cooperation with the Intelligence Advanced Research Project Activity (IARPA), conducted an open challenge on automatic speech recognition (ASR) technology for low-resource languages on a challenging data type - conversational telephone speech. The OpenASR20 Challenge was offered for ten low-resource languages - Amharic, Cantonese, Guarani, Javanese, Kurmanji Kurdish, Mongolian, Pashto, Somali, Tamil, and Vietnamese. A total of nine teams from five countries fully participated, and 128 valid submissions were scored. This paper gives an overview of the challenge setup and procedures, as well as a summary of the results. The results show overall high word error rate (WER), with the best results on a severely constrained training data condition ranging from 0.4 to 0.65, depending on the language. ASR with such limited resources remains a challenging problem. Providing a computing platform may be a way to level the playing field and encourage wider participation in challenges like OpenASR.