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<Paper uid="H90-1036">
  <Title>A Rapid Match Algorithm for Continuous Speech Recognition</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes an algorithm for performing rapid match on continuous speech that makes it possible to recognize sentences from an 842 word vocabulary on a desk-top 33 megahertz 80486 computer in near real time. This algorithm relies on a combination of smoothing and linear segmentation together with the notion of word start groups.</Paragraph>
    <Paragraph position="1"> It appears that the total computation required grows more slowly than linearly with the vocabulary size, so that larger vocabularies appear feasible, with only moderately enhanced hardware. The rapid match algorithm described here is closely related to the one that is used in DragonDictate, Dragon's commercial 30,000 word discrete utterance recognizer. null rapid match module to obtain a short list of plausible extensions. null The key ideas that the algorithm relies on are linear segmentation, smoothing, acoustic clustering, and word start groupings. In subsequent sections we shall elaborate on these ideas and explain their role in rapid match. We shall then report on some empirical results, having to do with a particular task that Dragon has chosen to use for development purposes: the dictation of mammography reports, using a vocabulary of 842 words.</Paragraph>
    <Paragraph position="2"> Other rapid match algorithms that are quite different in character have also been described in the literature \[2\], \[3\], and \[4\].</Paragraph>
  </Section>
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