English to Japanese spoken language translation system for classroom lectures

Author

Ferdiansyah, Veri ; Nakagawa, Sachiko

Author_Institution

Dept. of Comput. Sci. & Eng., Toyohashi Univ. of Technol., Toyohashi, Japan

fYear

2014

fDate

20-21 Aug. 2014

Firstpage

34

Lastpage

38

Abstract

This paper presents our attempt to create English automatic speech recognition (ASR) and English to Japanese statistical machine translation system (SMT). We used MIT OpenCourseWare lectures as our test lecture corpus. Wall Street Journal (WSJ) corpus adapted with MIT OpenCourseWare lectures was used as our acoustic model. MIT OpenCourseWare lecture transcriptions were utilized to create our language model. As for the parallel corpus, we used TED Talks and Japanese-English News Article Alignment Data (JENAAD). Our proposed ASR system can achieve 32.1% 0word error rate (WER) and our SMT system can achieve 10.95 BLEU.

Keywords

courseware; language translation; speech recognition; ASR system; English automatic speech recognition; English-to-Japanese statistical machine translation system; JENAAD; JapaneseEnglish News Article Alignment Data; MIT OpenCourseWare lectures; SMT system; TED Talks; WER; WSJ corpus; Wall Street Journal corpus; acoustic model; classroom lectures; lecture transcriptions; word error rate; Acoustics; Adaptation models; Data models; Hidden Markov models; Informatics; Speech; Speech recognition; MIT OCW; automatic speech recognition; classroom lectures; machine translation;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014 International Conference of

Conference_Location

Bandung

Print_ISBN

978-1-4799-6984-5

Type

conf

DOI

10.1109/ICAICTA.2014.7005911

Filename

7005911