TAILIEUCHUNG - Báo cáo khoa học: "Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology"

This paper presents an algorithm for learning the probabilities of optional phonological rules from corpora. The algorithm is based on using a speech recognition system to discover the surface pronunciations of words in corpora; using an automatic system obviates expensive phonetic labeling by hand. We describe the details of our algorithm and show the probabilities the system has learned for ten common phonological rules which model reductions and coarticulation effects. | Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology Gary Tajchman Daniel Jurafsky and Eric Fosler International Computer Science Institute and University of California at Berkeley taj chman j urafsky fosler @icsi. Abstract This paper presents an algorithm for learning the probabilities of optional phonological rules from corpora. The algorithm is based on using a speech recognition system to discover the surface pronunciations of words in speech corpora using an automatic system obviates expensive phonetic labeling by hand. We describe the details of our algorithm and show the probabilities the system has learned for ten common phonological rules which model reductions and coarticulation effects. These probabilities were derived from a corpus of 7203 sentences of read speech from the Wall Street Journal and are shown to be a reasonably close match to probabilities from phonetically hand-transcribed data TIMIT . Finally we analyze the probability differences between rule use in male versus female speech and suggest that the differences are caused by differing average rates of speech. - 1 Introduction Phonological rules have formed the basis of phonological theory for decades although their form and their coverage of the data has changed over the years. Until recently however it was difficult to determine the relationship between hand-written phonological rules and actual speech data. The current availability of large speech corpora and pronunciation dictionaries has allowed US to connect rules and speech in much tighter ways. For example a number of algorithms have recently been proposed which automatically induce phonological rules from dictionaries or corpora Gasser 1993 Ellison 1992 Daelemans et al. 1994 . While such algorithms have successfully induced syllabicity or harmony constraints or simple oblig- Currently at Voice Processing Corp 1 Main St Cambridge MA 02142 tajchman@ atory .

• Báo cáo khoa học
• Learning Phonological Rule
• Probabilities from Speech Corpora
• Exploratory Computational Phonology
• 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
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• 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
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• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
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• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
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