Jejueo Datasets for Machine Translation and Speech Synthesis
This provides essential datasets for Jejueo revitalization, addressing a gap for language and machine learning communities, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of computational resources for Jejueo, a critically endangered language, by constructing two new datasets: Jejueo Interview Transcripts (JIT) with 170k+ parallel sentences and Jejueo Single Speaker Speech (JSS) with 10k audio files, and used them to build neural machine translation and speech synthesis systems.
Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated by this, we construct two new Jejueo datasets: Jejueo Interview Transcripts (JIT) and Jejueo Single Speaker Speech (JSS). The JIT dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences, and the JSS dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file. Subsequently, we build neural systems of machine translation and speech synthesis using them. All resources are publicly available via our GitHub repository. We hope that these datasets will attract interest of both language and machine learning communities.