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<Paper uid="W04-2305">
  <Title>Combining Acoustic Confidences and Pragmatic Plausibility for Classifying Spoken Chess Move Instructions</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Domain and Data Collection
</SectionTitle>
    <Paragraph position="0"> The domain of our research are spoken chess move instructions. We chose this scenario as a testbed for our approach for three main reasons. First, we can use move evaluation scores computed by a computer chess program as a measure of the pragmatic plausibility of hypotheses. Second, the domain is simple and allows us to collect data in a controlled way (e.g. we can control for player strength), and third, the domain is restricted in the sense that there is only a finite set of possible legal moves in every situation. Similar considerations already let researchers in the 1970s choose chess-playing as an example scenario for the HEARSAY integrated speech understanding system (Reddy and Newell, 1974).</Paragraph>
    <Paragraph position="1"> We collected spoken chess move instructions in a small experiment from six pairs of chess players. All subjects were German native speakers and familiar with the rules of chess. The subject's task was to re-play chess games (given to them as graphical representations) by instructing each other to move pieces on the board.</Paragraph>
    <Paragraph position="2"> Altogether, we collected 1978 move instructions under different experimental conditions (e.g. strong games vs.</Paragraph>
    <Paragraph position="3"> weak games) in the following four data sets: 1) language model, 2) training, 3) development, and 4) test.</Paragraph>
    <Paragraph position="4"> The recordings of the language model games were transcribed and served to construct a context free recognition grammar for the Nuance 8.02 speech recogniser which was then used to process all other move instructions with 10-best output.</Paragraph>
  </Section>
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