<?xml version="1.0" standalone="yes"?>
<Paper uid="P05-1042">
  <Title>A Dynamic Bayesian Framework to Model Context and Memory in Edit Distance Learning: An Application to Pronunciation Classification</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Edit distance (ED) is a common measure of the similarity between two strings. It has a wide range of applications in classification, natural language processing, computational biology, and many other fields. It has been extended in various ways; for example, to handle simple (Lowrance and Wagner, 1975) or (constrained) block transpositions (Leusch et al., 2003), and other types of block operations (Shapira and Storer, 2003); and to measure similarity between graphs (Myers et al., 2000; Klein, 1998) or automata (Mohri, 2002).</Paragraph>
    <Paragraph position="1"> [?]This material was supported by NSF under Grant No. ISS0326276. null Another important development has been the use of data-driven methods for the automatic learning of edit costs, such as in (Ristad and Yianilos, 1998) in the case of string edit distance and in (Neuhaus and Bunke, 2004) for graph edit distance.</Paragraph>
    <Paragraph position="2"> In this paper we revisit the problem of learning string edit distance costs within the Graphical Models framework. We apply our method to a pronunciation classification task and show significant improvements over the standard Levenshtein distance (Levenshtein, 1966) and a previous transducer-based learning algorithm.</Paragraph>
    <Paragraph position="3"> In section 2, we review a stochastic extension of the classic string edit distance. We present our DBN-based edit distance models in section 3 and show results on a pronunciation classification task in section 4. In section 5, we discuss the computational aspects of using our models. We end with our conclusions and future work in section 6.</Paragraph>
  </Section>
class="xml-element"></Paper>