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.

Killkan 是第一个专注于 Kichwa 语（厄瓜多尔的一种土著语言）的自动语音识别（ASR）数据集，包含约 4 小时的音频及转录、西班牙语翻译和形态句法注释等信息，旨在为这种极度资源匮乏和濒危的语言提供自然语言处理应用的资源建设。本研究还通过基于语料库的分析，特别关注 Kichwa 语的合成构词和与西班牙语的频繁语码混用，实验证明即使数据集规模较小，也能实现可靠质量的 Kichwa 语 ASR 系统的开发。该数据集、ASR 模型和开发所使用的代码将公开提供，积极展示了对资源建设及其在资源匮乏语言及其社区中的应用。