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<Paper uid="W04-3012">
  <Title>Word level confidence measurement using semantic features. In Proceedings of ICASSP, Hong Kong, April.</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present the results of experiments aimed at assigning domains to speech recognition hypotheses (SRH). The methods rely on high-level linguistic representations of SRHs as sets of ontological concepts. We experimented with two domain models and evaluated their performance against a statistical, word-based model. Our hand-annotated and tf*idf-based models yielded a precision of 88,39% and 82,59% respectively, compared to 93,14% for the word-based base-line model. These results are explained in terms of our experimental setup.</Paragraph>
  </Section>
class="xml-element"></Paper>