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<Paper uid="H94-1055">
  <Title>Phonological Parsing for Bi-directional Letter-to-Sound/Sound-to-Letter Generation 1</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> This paper describes a trainable probabilistic system for reversible letter-to-sound/sound-to-letter generation.</Paragraph>
    <Paragraph position="1"> Sound-to-letter generation is a crucial aspect in the problem of automatic detection/incorporation of new words, which is in turn critical for the development of large vocabulary speech understanding systems. Moreover, letter-to-sound generation will continue to be important for speech output, especially in applications such as reading machines. To successfully achieve our goal, several important issues must be addressed. First, what should be the inventory of linguistic or lexical units for describing English orthographic-phonological regularities? Second, how should these units be incorporated into the representation of English orthography and phonology? Third, what algorithms can be used to synthesize and analyze the spelling and pronunciation of an English word  grant from Apple Computer Inc.</Paragraph>
    <Paragraph position="2"> in terms of these lexical units? These three issues will be addressed in detail in the following when we describe our approach and report on our system's performance for both letter-to-sound \[1\] and sound-to-letter generation \[2\]. The novel features of our approach include the reversibility of the combined parsing and generative processes, the ability to provide multiple output hypotheses, the capability of handling uncertainty in the input, as well as our treatment of non-parsab!e words.</Paragraph>
  </Section>
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