TAILIEUCHUNG - Báo cáo khoa học: "NICT-ATR Speech-to-Speech Translation System"

This paper describes the latest version of speech-to-speech translation systems developed by the team of NICT-ATR for over twenty years. The system is now ready to be deployed for the travel domain. A new noise-suppression technique notably improves speech recognition performance. Corpus-based approaches of recognition, translation, and synthesis enable coverage of a wide variety of topics and portability to other languages. recent progress. | NICT-ATR Speech-to-Speech Translation System Eiichiro Sumita Tohru Shimizu Satoshi Nakamura National Institute of Information and Communications Technology ATR Spoken Language Communication Research Laboratories 2-2-2 Hikaridai Keihanna Science City Kyoto 619-0288 Japan Abstract This paper describes the latest version of speech-to-speech translation systems developed by the team of NICT-ATR for over twenty years. The system is now ready to be deployed for the travel domain. A new noise-suppression technique notably improves speech recognition performance. Corpus-based approaches of recognition translation and synthesis enable coverage of a wide variety of topics and portability to other languages. 1 Introduction Speech recognition speech synthesis and machine translation research started about half a century ago. They have developed independently for a long time until speech-to-speech translation research was proposed in the 1980 s. The feasibility of speech-to-speech translation was the focus of research at the beginning because each component was difficult to build and their integration seemed more difficult. After groundbreaking work for two decades corpus-based speech and language processing technology have recently enabled the achievement of speech-to-speech translation that is usable in the real world. This paper introduces at ACL 2007 the state-of-the-art speech-to-speech translation system developed by NICT-ATR Japan. 2 SPEECH-TO-SPEECH TRANSLATION SYSTEM A speech-to-speech translation system is very large and complex. In this paper we prefer to describe recent progress. Detailed information can be found in 1 2 3 and their references. Speech recognition To obtain a compact accurate model from corpora with a limited size we use MDL-SSS 4 and composite multi-class N-gram models 5 for acoustic and language modeling respectively. MDL-SSS is an algorithm that automatically determines the appropriate .

• Báo cáo khoa học
• NICT ATR Speech to Speech
• Translation System
• Eiichiro Sumita Tohru Shimizu Satoshi Nakamura
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học