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<Paper uid="H94-1015">
  <Title>SPEECH RECOGNITION USING A STOCHASTIC LANGUAGE MODEL INTEGRATING LOCAL AND GLOBAL CONSTRAINTS</Title>
  <Section position="9" start_page="90" end_page="90" type="concl">
    <SectionTitle>
6. CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> In this paper, a speech recognition system using a new stochastic language model that integrates local and global linguistic constraints was proposed. Function word bigrams and content word bigrams were introduced to capture global syntactic and semantic constraints, and combined with a conventional word bigram model. The number of parameters was reduced by decomposing local and global dependency.</Paragraph>
    <Paragraph position="1"> Continuous speech recognition based on the time-synchronous Viterbi decoding algorithm with the proposed language model incorporated into it was presented, and speaker-dependent speech recognition experiments were conducted. Although the improvements in performance over the conventional bigram model are rather modest, results show that the proposed model has the capability to capture Linguistic constraints effectively.</Paragraph>
    <Paragraph position="2">  The assumptions made to reduce parameters do not degrade perplexity, but their validity needs to be verified from the linguistic point of view. The number of parameters is reduced in the proposed model, but the size of database we used is still not large enough to estimate the statistics in the model. More data would be necessary to evaluate the effectiveness of the proposed model. The use of part of speech or word equivalence classes generated automatically (for example, \[10\]) could help to increase the robustness of the estimates obtained from the limited size of the corpora.</Paragraph>
    <Paragraph position="3"> In the future, we plan to further investigate the effective utilization of linguistic knowledge as well as statistical approaches to ex/ract more useful global constraints.</Paragraph>
  </Section>
class="xml-element"></Paper>