Speech-Based Retrieval Using Semantic Co-Occurrence Filtering

Speech-Based Retrieval Using Semantic Co-Occurrence Filtering Julian Kupiec, Don Kimber and Vijay Balasubramanian Xerox Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, CA 94304 ABSTRACT In this paper we demonstrate that speech recognition can be effectively applied to information retrieval (IR) applications. Our system exploits the fact that the intended words of a spo- ken query tend to co-occur in text documents in close proxim- ity whereas word combinations that are the result of recogni- tion errors are usually not semantically correlated and thus do not appear together. Termed "Semantic Co-occurrence Filtering" this enables the system to simultaneously disam- biguate word hypotheses and find relevant text for retrieval. The system is built by integrating standard IR and speech recognition techniques. An evaluation of the system is pre- seated and we discuss several refinements to the functionality. 1. Introduction In applying speech recognition techniques to retrieve infor- mation from large unrestricted text corpora, several issues immediately arise. The recognition vocabulary is very large (being the same size as the corpus vocabulary). Each new corpus may cover a different domain, requiring new spe- cialized vocabulary. Furthermore the constraint afforded by domain-dependent language models may be precluded due to the expense involved in constructing them. One approach to these problems obviates the need for any word vocabulary to be defined [1, 2]. This is done by defining a phonetic inventory based on phonetically stable sub-word units which have corresponding orthographic counterparts. This scheme has the potential advantage that both speech and text can be indexed in terms of the same units and thus speech might be used to access text and vice-versa. Sub- word units are considered to be independent and matching is performed using vector-space similarity measures. Our concern in this paper is to provide speech access to text and our approach differs from the former in that whole words are used to constrain matching; we believe this to be more effective than splitting words into smaller independent units. We use boolean retrieval with proximity constraints rather than vector-space measures. Our approach also accommo- dates standard phonetic alphabets (we employ a set of 39 phones in contrast to the former technique which uses about 1000 phonetic units). To demonstrate the feasibility of our approach we have im- plemented a prototype. The user speaks each word of a query separately and is presented with the most relevant titles, each accompanied by the relevant word hypotheses. The combina- tion of speech processing and retrieval currently takes about 373 20-30 seconds. Figure 1 shows all titles produced for the query "Maltese Falcon". The IR system acts as a novel kind of language model. The text corpus is used directly; it is not necessary to pre-compute statistical estimates, only to index the text as appropriate for the retrieval system. 1. Maltese Falcon, The (maltese falcon) 2. Astor, Mary (maltese falcon) 3. film noir (maltese falcon) 4. Bogart, Humphrey (maltese falcon) 5. Huston, John (maltese falcon) 6. Hammett, Dashiell (maltese falcon) 7. Louis XV, King …

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