Integrated Techniques for Phrase Extraction From Speech

INTEGRATED TECHNIQUES FOR PHRASE EXTRACTION FROM SPEECH Marie Meteer J. Robin Rohlicek BBN Systems and Technologies Cambridge, Massachusetts 02138 mmeteer@bbn.com rohlicek@bbn.com ABSTRACT We present an integrated approach to speech and natural language processing which uses a single parser to create training for a statistical speech recognition component and for interpreting recognized text. On the speech recognition side, our innovation is the use of a statistical model combining N-gram and context-free grammars. On the natural language side, our innovation is the integration of parsing and semantic interpretation to build references for only targeted phrase types. In both components, a semantic grammar and partial parsing facilitate robust processing of the targeted portions of a domain. This integrated approach introduces as much linguistic structure and prior statistical information as is available while maintaining a robust full-coverage statistical language model for recognition. In addition, our approach facilitates both the direct detection of linguistic constituents within the speech recognition algorithms and the creation of semantic interpretations of the recognized phrases. 1. INTRODUCTION Language modeling for speech recognition has focused on robustness, using statistical techniques such as n-grams, whereas work in language understanding and information extraction has relied more on rule based techniques to leverage linguistic and domain information. However, the knowledge needed in these two components of a speech language system is actually very similar. In our work, we take an integrated approach, which uses a single grammar for both language modeling and language understanding for targeted portions of the domain and uses a single parser for both training the language model and extracting information from the output of the recognizer. The goal of our work is provide speech recognition capabilities that are analogous to those of information extraction systems: given large amounts of (often low quality) speech, selectively interpret particular kinds of information. For example, in the air traffic control domain, we want to determine the flight IDs, headings, and altitudes of the planes, and to ignore other information, such as weather and ground movement. The following is a summary of the main techniques we use in our approach: Integration of N-gram and context free grammars for speech recognition: While statistically based Markov- chain language models (N-gram models) have been shown to be effective for speech recognition, there is, in general, more structure present in natural language than N-gram models can capture. Linguistically based approaches that use statistics to provide probabilities for word sequences that are accepted by a grammar typically require a full coverage grammar, and therefore are only useful for constrained sublanguages. In the work presented here, we combine linguistic structure in the form of a partial-coverage phrase structure grammar with statistical N-gram techniques. The result is a robust statistical grammar which explicitly incorporates syntactic and semantic structure. A second feature of our approach is that we are able to determine which portions of the text were recognized by the phrase grammars, allowing us to isolate these phrases for more processing, thus reducing the overall time needed for interpretation. Partial parsing: It …

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