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<Paper uid="W04-3003">
  <Title>Interactive Machine Learning Techniques for Improving SLU Models</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Spoken language understanding is a critical component of automated customer service applications. Creating effective SLU models is inherently a data driven process and requires considerable human intervention. We describe an interactive system for speech data mining. Using data visualization and interactive speech analysis, our system allows a User Experience (UE) expert to browse and understand data variability quickly. Supervised machine learning techniques are used to capture knowledge from the UE expert. This captured knowledge is used to build an initial SLU model, an annotation guide, and a training and testing system for the labelers. Our goal is to shorten the time to market by increasing the efficiency of the process and to improve the quality of the call types, the call routing, and the overall application.</Paragraph>
  </Section>
class="xml-element"></Paper>