The ATR multilingual speech-to-speech translation system

被引：62
|
作者：
Nakamura, S [1 ]
Markov, K [1 ]
Nakaiwa, H [1 ]
Kikui, G [1 ]
Kawai, H [1 ]
Jitsuhiro, T [1 ]
Zhang, JS [1 ]
Yamamoto, H [1 ]
Sumita, E [1 ]
Yamamoto, S [1 ]
机构：
[1] ATR Spoken Language Translat Res Labs, Kyoto 6190288, Japan
来源：
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2006年 / 14卷 / 02期
关键词：
example-based machine translation (EBMT); minimum description length (MDL); multiclass language model; speech-to-speech translation (S2S); statistical machine translation (SMT); successive state splitting (SSS); text-to-speech (TTS) conversion;
D O I：
10.1109/TSA.2005.860774
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
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.
引用
收藏
页码：365 / 376
页数：12
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