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<Paper uid="P06-1140">
  <Title>Learning to Say It Well: Reranking Realizations by Predicted Synthesis Quality</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a method for adapting a language generator to the strengths and weaknesses of a synthetic voice, thereby improving the naturalness of synthetic speech in a spoken language dialogue system. The method trains a discriminative reranker to select paraphrases that are predicted to sound natural when synthesized.</Paragraph>
    <Paragraph position="1"> The ranker is trained on realizer and synthesizer features in supervised fashion, using human judgements of synthetic voice quality on a sample of the paraphrases representative of the generator's capability.</Paragraph>
    <Paragraph position="2"> Results from a cross-validation study indicate that discriminative paraphrase reranking can achieve substantial improvements in naturalness on average, ameliorating the problem of highly variable synthesis quality typically encountered with today's unit selection synthesizers.</Paragraph>
  </Section>
class="xml-element"></Paper>