TAILIEUCHUNG - Báo cáo khoa học: "Data-Oriented Methods for Grapheme-to-Phoneme Conversion"

It is traditionally assumed that various sources of linguistic knowledge and their interaction should be formalised in order to be able to convert words into their phonemic representations with reasonable accuracy. We show that using supervised learning techniques, based on a corpus of transcribed words, the same and even better performance can be achieved, without explicit modeling of linguistic knowledge. In this paper we present two instances of this approach. | Data-Oriented Methods for Grapheme-to-Phoneme Conversion Antal van den Bosch and Walter Daelemans ITK Institute for Language Technology and Al Tilburg University . Box 90153 NL-5000 LE Tilburg Tel 31 13 663070 Email antalb@ walter@ Abstract It is traditionally assumed that various sources of linguistic knowledge and their interaction should be formalised in order to be able to convert words into their phonemic representations with reasonable accuracy. We show that using supervised learning techniques based on a corpus of transcribed words the same and even better performance can be achieved without explicit modeling of linguistic knowledge. In this paper we present two instances of this approach. A first model implements a variant of instance-based learning in which a weighed similarity metric and a database of prototypical exemplars are used to predict new mappings. In the second model grapheme-to-phoneme mappings are looked up in a compressed text-to-speech lexicon table lookup enriched with default mappings. We compare performance and accuracy of these approaches to a connectionist backpropagation approach and to the linguistic knowledge-based approach. 1 Introduction Grapheme-to-phoneme conversion is a central task in any text-to-speech reading aloud system. Given an alphabet of spelling symbols graphemes and an alphabet of phonetic symbols a mapping should be achieved transliterating strings of graphemes into strings of phonetic symbols. It is well known that this mapping is difficult because in general not all graphemes are realised in the phonetic transcription and the same grapheme may correspond to different phonetic symbols depending on context. It is traditionally assumed that various sources of linguistic knowledge and their interaction should be formalised in order to be able to convert words into their phonemic representations with reasonable accuracy. Although different researchers propose different knowledge structures consensus seems

• Báo cáo khoa học
• Data Oriented Methods
• Grapheme to Phoneme Conversion
• Antal van den Bosch
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln