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<Paper uid="H89-2020">
  <Title>A Simple Statistical Class Grammar for Measuring Speech Recognition Performance</Title>
  <Section position="7" start_page="148" end_page="149" type="concl">
    <SectionTitle>
5 CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> We have described the development of a statistical first-order class grammar. The structure of this grammar allows for relatively easy development of a new grammar for a new task domain. The grammar provides for full coverage of the task domain, even if all possible class sequences are not observable in the data used to train the grammar probabilities. It also provides a method for adjusting the perplexity of the grammar by varying the number of classes in the grammar.</Paragraph>
    <Paragraph position="1"> We recommend that this grammar should be made another standard grammar for the DARPA speech commumty. We believe that this grammar could extend the life of the Resource Management task by decreasing the recogmtion system's performance while still placing re.</Paragraph>
    <Paragraph position="2"> straints on the possible sequencing of words in a meaningful way. Decreasing the recognition performance will allow the (statistically significant) measurement of small system improvements without needing to increase the size of the evaluation test. We also recommend that this grammar replace the word-pair for official system evaluations.</Paragraph>
  </Section>
class="xml-element"></Paper>