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<Paper uid="W99-0902">
  <Title>The applications of unsupervised learning to Japanese grapheme-phoneme alignment</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we adapt the TF-IDF model to the Japanese grapheme-phoneme alignment task, by way of a simple statistical model and an incremental learning method. In the incremental learning method, grapheme-phoneme alignment paradigms are disambiguated one at a time according to the relative plausibility of the highest scoring alignment schema, and the statistical model is re-trained accordingly. On limited evaluation, the learning method achieved an accuracy of 93.28%, representing a slight improvement over a baseline rule-based method.</Paragraph>
  </Section>
class="xml-element"></Paper>