TAILIEUCHUNG - Báo cáo khoa học: "Practical Issues in Compiling Typed Unification Grammars for Speech Recognition"

Current alternatives for language modeling are statistical techniques based on large amounts of training data, and hand-crafted context-free or finite-state grammars that are difficult to build and maintain. One way to address the problems of the grammar-based approach is to compile recognition grammars from grammars written in a more expressive formalism. While theoretically straight-forward, the compilation process can exceed memory and time bounds, and might not always result in accurate and efficient speech recognition. We will describe and evaluate two approaches to this compilation problem. . | Practical Issues in Compiling Typed Unification Grammars for Speech Recognition John Dowding Beth Ann Hockey Jean Mark Gawron RIACS RIALIST Group Dept. of Linguistics NASA Ames Research Center San Diego State University Moffett Field CA 94035 San Diego CA jdowding@ gawron@ bahockey@ Christopher Culy SRI International 333 Ravenswood Avenue Menlo Park CA 94025 culy@ Abstract Current alternatives for language modeling are statistical techniques based on large amounts of training data and hand-crafted context-free or finite-state grammars that are difficult to build and maintain. One way to address the problems of the grammar-based approach is to compile recognition grammars from grammars written in a more expressive formalism. While theoretically straight-forward the compilation process can exceed memory and time bounds and might not always result in accurate and efficient speech recognition. We will describe and evaluate two approaches to this compilation problem. We will also describe and evaluate additional techniques to reduce the structural ambiguity of the language model. 1 Introduction Language models to constrain speech recognition are a crucial component of interactive spoken language systems. The more varied the language that must be recognized the more critical good language modeling becomes. Research in language modeling has heavily favored statistical approaches Cohen 1995 Ward 1995 Hu et al. 1996 Iyer and Ostendorf 1997 Bellegarda 1999 Stolcke and Shriberg 1996 while hand-coded finite-state or context-free language models dominate the commercial sector Nuance 2001 SpeechWorks 2001 TellMe 2001 BeVocal 2001 HeyAnita 2001 W3C 2001 . The difference revolves around the availability of data. Research systems can achieve impressive performance using statistical language models trained on large amounts of domain-targeted data but for many domains sufficient data is not available. Data may be unavailable because the domain

• Báo cáo khoa học
• Practical Issues
• Compiling Typed Unification Grammars
• Speech Recognition
• báo cáo khoa học
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• Methods and Practical Issues
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