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<Paper uid="W97-0615">
  <Title>Filtering Errors and Repairing Linguistic Anomalies for Spoken Dialogue Systems</Title>
  <Section position="7" start_page="80" end_page="80" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> The results enlighten the repairing capacities of a couple filtering module/robust parsing module. In addition this couple presents some original desirable features that we intend to push further. First, although the parser belongs to the family of robust parsers -since it can process ill-formed sentence- it is still able to reject a subset of ill-formed sentences, which may be produced by a recognizer. Second, thanks to the lexical recovery from word candidates in the N-best hypothesis, the spoken input can be decoded further.</Paragraph>
    <Paragraph position="1"> The scoring module can be seen as achieving not so much a filtering than a narrowing of the search space of recognition candidates. However, the approach has limitations: the parser cannot handle a large number of candidates so that the number of N-best must be limited and hence the correct candidates sometimes missed.</Paragraph>
    <Paragraph position="2"> Moreover, spurious hypothesis generated along the passes are still hard to eliminate. This suggests the need for cross-checking with other knowledge sources, like statistical cues derived from text corpora or from recognition errors corpora.</Paragraph>
    <Paragraph position="3"> To sum up, our work described an integration of speech recognition and language processing which is independent from a given recognition system. The basic idea was to make use of available acoustic information in order to point out a limited set of words to suspect --especially inserted words- and to exploit the potential of linguistic knowledge in order to repair the best sentence hypothesis. It can serve as a basis for many more developments.</Paragraph>
  </Section>
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