低資源言語の音声認識のためのメタ情報の活用

京都大学新制・課程博士博士(情報学)甲第24729号情博第817号新制||情||137(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 河原 達也, 教授 黒橋 禎夫, 教授 森 信介学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

TriECCC: Trilingual Corpus of the Extraordinary Chambers in the Courts of Cambodia for Speech Recognition and Translation Studies

This paper presents an extended work on the trilingual spoken language translation corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC), namely TriECCC. TriECCC is a simultaneously spoken language translation corpus with parallel resources of speech and text in three languages: Khmer, English, and French. This corpus has approximately [Formula: see text] thousand utterances, approximately [Formula: see text], [Formula: see text], and [Formula: see text] h in length of speech, and [Formula: see text], [Formula: see text] and [Formula: see text] million words in text, in Khmer, English, and French, respectively. We first report the baseline results of machine translation (MT), and speech translation (ST) systems, which show reasonable performance. We then investigate the use of the ROVER method to combine multiple MT outputs and fine-tune the pre-trained English–French MT models to enhance the Khmer MT systems. Experimental results show that the ROVER is effective for combining English-to-Khmer and French-to-Khmer systems. Fine-tuning from both single and multiple parents shows the effective improvement on the BLEU scores for Khmer-to-English/French and English/French-to-Khmer MT systems