Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information

TL;DR

This paper introduces Killkan, the first Kichwa speech dataset with morphosyntactic annotations, enabling initial ASR development for this endangered language despite limited data.

Contribution

The paper presents Killkan, a novel low-resource speech dataset for Kichwa with annotations, facilitating the development of the first ASR system for the language.

Findings

Enabled development of a reliable Kichwa ASR system

Analyzed Kichwa's agglutinative morphology and code-switching

Made dataset, model, and code publicly available

Abstract

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.