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<Paper uid="H89-1043">
  <Title>INTEGRATING SPEECH AND NATURAL-LANGUAGE PROCESSING</Title>
  <Section position="3" start_page="0" end_page="243" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> We report on an innovative and highly effective architecture for integrating speech and natural-language (NL) processing. This architecture takes full advantage of the linguistic constraints supplied by an NL processor, yet it maintains a very tractable recognition search space and appears to require dramatically less computation by the NL processor than do other approaches.</Paragraph>
    <Paragraph position="1"> It is useful to begin by recalling why one would want to integrate speech and NL processing. There are two primary reasons why this is desirable. First, there are many applications of spoken-language processing that require not just recognition but understanding, such as spoken-language systems for database query and other computer interface applications. Second, constraints on natural language can be used to reduce the speech recognition search space and therefore improve recognition accuracy.</Paragraph>
    <Paragraph position="2"> To take an example from our own system, one of the sentences in the 1987 DARPA resource management speaker-independent test set is the following: What is the ETA at her destination of Fanning? Without any source of grammatical constraints (perplexity -- 1000), one version of the SRI system recognizes this sentence as: Why added ETA at her destination of Fanning.</Paragraph>
    <Paragraph position="3"> This hypothesis contains three errors out of nine words in the original sentence. With the fairly modest degree of grammatical constraint represented by a perplexity-510 natural-language grammar, however, our system was able to recognize this sentence with no errors.</Paragraph>
    <Paragraph position="4"> The point is that even though the incorrect hypothesis represented a better phonetic and phonological match to the input signal according to the models used by our system, with an NL grammar the system was able to rule out the incorrect hypothesis and choose the correct, although lower-scoring, hypothesis. While this is, of course, a carefully selected example of our system's performance, it is a good illustration of what we are trying to acheive.</Paragraph>
  </Section>
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