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<Paper uid="P05-1063">
  <Title>Discriminative Syntactic Language Modeling for Speech Recognition</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second &amp;quot;reranking&amp;quot; model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perceptron algorithm. We describe experiments on the Switchboard speech recognition task. The syntactic features provide an additional 0.3% reduction in test-set error rate beyond the model of (Roark et al., 2004a; Roark et al., 2004b) (significant at p &lt; 0.001), which makes use of a discriminatively trained n-gram model, giving a total reduction of 1.2% over the baseline Switchboard system.</Paragraph>
  </Section>
class="xml-element"></Paper>