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<Paper uid="P95-1001">
  <Title>Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology</Title>
  <Section position="9" start_page="6" end_page="6" type="concl">
    <SectionTitle>
5 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> Although the paradigm of exploratory computational phonology is only in its infancy, we believe our rule-probability estimation algorithm to be a new and useful instance of the use of probabilistic techniques and spoken-language corpora in computational linguistics. In Tajchman et al. (1995) we report on the results of our algorithm on speech recognition performance. We plan in future work to address a number of shortcomings of these experiments, for example including some spontaneous speech corpora, and looking at a wider variety of rules.</Paragraph>
    <Paragraph position="1"> In addition, we have extended our algorithm to induce new pronunciations which generalize over pronunciations seen in the corpus (Wooters &amp; Stolcke 1994). We now plan to augment our probability estimation to use the pronunciations from this new HMM-induction-based generalization step. This will require extending our tag-based probability estimation step to parse the phone strings from the forcedViterbi. null In other current work we have also been using this algorithm to model the phonological component of the accent of non-native speakers. Finally, we hope in future work to be able to combine our rule-based approach with more bottom-up methods like the decision-tree or phonological parsing algorithms to induce rules as well as merely training their probabilities* null</Paragraph>
  </Section>
class="xml-element"></Paper>