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<Paper uid="W97-1008">
  <Title>What makes a word: Learning base units in Japanese for speech recognition</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> What defines a word when there are no spaces in a written language? Words, as they are known in English and other western languages, are the basic units of recognition in most CSR systems, but when a language is written as a string of characters with no white space, how does one go about specifying the fundamental units that must be recognized? Mapping onto English-style words is one solution, but an artificial one, and may hide natural characteristics of Japanese that can be important in recognition. Recognizing phonemes, or short phoneme clusters, is another option, but recognition accuracy can improve when we have longer phoneme strings to work with; acoustic confusability decreases and a long word is a more useful predictor of subsequent words than a single syllable. Automatic segmenting tools eliminate an often inconsistent manual segmentation step, but are generally based on morphological analysis, which can produce units smaller than are desirable for speech recognition. Certainly, there exist words as can be looked up in a dictionary, but when a language is as heavily inflected as Japanese is, that only solves part of the problem. In this paper we describe an automatic process for learning base units in Japanese and discuss its usefulness for speech recognition.</Paragraph>
  </Section>
class="xml-element"></Paper>