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<Paper uid="W97-0615">
  <Title>Filtering Errors and Repairing Linguistic Anomalies for Spoken Dialogue Systems</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Spoken dialogue systems enable people to interact with computers using speech. However, a key challenge for such interfaces is to couple successfully automatic speech recognition (ASR) and natural language processing modules (NLP) given their limits.</Paragraph>
    <Paragraph position="1"> Several collaboration modalities between ASR and NLP have been investigated. On the one hand, the speech recognition task can benefit from linguistic decision to uncover the correct utterance, see (Rayner et al., 1994) among others. On the other hand, NLP components can be robust with respect to recognition errors. The straightforward approach is to be robust by focusing only on informative words (Lamel et al., 1995; Meteer and Rohlicek, 1994). By nature, it misses some existing information in the sentence and it can be misled in case of errors on informative words. A more controlled robustness is expected with a complete linguistic analysis (Young, 1994; Hanrieder and GSrz, 1995; Dowding et al., 1994). In a practical application, a dialogue module *with Lab. CLIPS IMAG, Grenoble twith Dept. Signal, ENST Paris can then handle interactive recovery, as illustrated by (Suhm, Myers, and Waibel, 1996).</Paragraph>
    <Paragraph position="2"> The current work attempts to repair misrecognitions by mobilising available acoustic cues and by using linguistic abstraction and syntactico-semantic predictions. We present a filtering method and a repairing parsing strategy which fit in a complete system architecture.</Paragraph>
    <Paragraph position="3"> An advantage of our approach is the use of a core module that is independent from any application.</Paragraph>
    <Paragraph position="4"> Another advantage, for real applications, is to be aware of the expected performances of the ASR systems. Indeed, there are obstacles that prevent ASR systems to be fully reliable. In particular, the decoding algorithms enforce models which do not exploit all linguistic knowledge, mainly due to computational complexity. This hinders somehow the decoding so that the right solution is sometimes just not available.</Paragraph>
  </Section>
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