Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
NICT-ATR speech-to-speech translation system
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
A walk on the other side: adding statistical components to a transfer-based translation system
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Hyperbolic structure of fundamental frequency contour
Proceedings of the 3rd International Universal Communication Symposium
Japanese Spontaneous Spoken Document Retrieval Using NMF-Based Topic Models
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Audio, Speech, and Language Processing
Learning Novel Objects for Extended Mobile Manipulation
Journal of Intelligent and Robotic Systems
Enriching machine-mediated speech-to-speech translation using contextual information
Computer Speech and Language
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In this paper, we describe the ATR multilingual speech-to-speech translation (S2ST) system, which is mainly focused on translation between English and Asian languages (Japanese and Chinese). There are three main modules of our S2ST system: large-vocabulary continuous speech recognition, machine text-to-text (T2T) translation, and text-to-speech synthesis. All of them are multilingual and are designed using state-of-the-art technologies developed at ATR. A corpus-based statistical machine learning framework forms the basis of our system design. We use a parallel multilingual database consisting of over 600 000 sentences that cover a broad range of travel-related conversations. Recent evaluation of the overall system showed that speech-to-speech translation quality is high, being at the level of a person having a Test of English for International Communication (TOEIC) score of 750 out of the perfect score of 990.