<?xml version="1.0" standalone="yes"?>
<Paper uid="W98-1223">
  <Title>Modularity in Inductively-Learned Word Pronunciation Systems *</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In leading morpho-phonological theories and state-of-the-art text-to-speech systems it is assumed that word pronunciation cannot be learned or performed without in-between analyses at several abstraction levels (e.g., morphological, graphemic, phonemic, syllabic, and stress levels). We challenge this assumption for the case of English word pronunciation. Using IGTR~B, an inductive-learning decision-tree algorithms, we train and test three word-pronunciation systems in which the number of abstraction levels (implemented as sequenced modules) is reduced from five, via three, to one. The latter system, classifying letter strings directly as mapping to phonemes with stress markers, yields signitlcemtly better generali~tion accuracies than the two multimodule systems. Analyses of empirical results indicate that positive utility etfects of sequencing modules are outweighed by cascading errors passed on between modules.</Paragraph>
  </Section>
class="xml-element"></Paper>