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<Paper uid="C04-1168">
  <Title>A Uni ed Approach in Speech-to-Speech Translation: Integrating Features of Speech Recognition and Machine Translation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Current translation systems are typically of a cascaded structure: speech recognition followed by machine translation. This structure, while explicit, lacks some joint optimality in performance since the speech recognition module and translation module are running rather independently. Moreover, the translation module of a speech translation system, a natural o spring of text-input based translation system, usually takes a single-best recognition hypothesis transcribed in text and performs standard text-based translation. Lots of supplementary information available from speech recognition, such as N-best recognition recognition hypotheses, likelihoods of acoustic and language models, is not well utilized in the translation process.</Paragraph>
    <Paragraph position="1"> The information can be e ective for improving translation quality if employed properly.</Paragraph>
    <Paragraph position="2"> The supplementary information can be exploited by a tight coupling of speech recognition and machine translation (Ney, 1999) or keeping the cascaded structure unchanged but using an integration model, log-linear model, to rescore the translation hypotheses. In this study the last approach was used due to its explicitness.</Paragraph>
    <Paragraph position="3"> In this paper we intended to improve speech translation by exploiting these information.</Paragraph>
    <Paragraph position="4"> Moreover, a number of advanced features from the machine translation module were also added in the models. All the features from the speech recognition and machine translation module were combined by the log-linear models seamlessly. null In order to test our results broadly, we used four automatic translation evaluation metrics: BLEU, NIST, multiple word error rate and position independent word error rate, to measure the translation improvement.</Paragraph>
    <Paragraph position="5"> In the following, in section 2 we introduce the speech translation system. In section 3, we describe the optimization algorithm used to nd the weight parameters in the log-linear model.</Paragraph>
    <Paragraph position="6"> In section 4 we demonstrate the e ectiveness of our technique in speech translation experiments. In the nal two sections we discuss the results and present our conclusions.</Paragraph>
  </Section>
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