Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information
This addresses the problem of resource scarcity for low-resource indigenous languages like Kichwa, enabling NLP applications for their communities, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of resources for automatic speech recognition (ASR) in the endangered Kichwa language by creating Killkan, the first dataset with approximately 4 hours of audio, transcriptions, translations, and morphosyntactic annotations, enabling the development of the first reliable ASR system for Kichwa.
This paper presents Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador. Kichwa is an extremely low-resource endangered language, and there have been no resources before Killkan for Kichwa to be incorporated in applications of natural language processing. The dataset contains approximately 4 hours of audio with transcription, translation into Spanish, and morphosyntactic annotation in the format of Universal Dependencies. The audio data was retrieved from a publicly available radio program in Kichwa. This paper also provides corpus-linguistic analyses of the dataset with a special focus on the agglutinative morphology of Kichwa and frequent code-switching with Spanish. The experiments show that the dataset makes it possible to develop the first ASR system for Kichwa with reliable quality despite its small dataset size. This dataset, the ASR model, and the code used to develop them will be publicly available. Thus, our study positively showcases resource building and its applications for low-resource languages and their community.