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<Paper uid="W06-3711">
  <Title>IBM MASTOR SYSTEM: Multilingual Automatic Speech-to-speech Translator *</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> Automatic speech-to-speech (S2S) translation breaks down communication barriers between people who do not share a common language and hence enable instant oral cross-lingual communication for many critical applications such as emergency medical care. The development of an accurate, efficient and robust S2S translation system poses a lot of challenges. This is especially true for colloquial speech and resource deficient languages.</Paragraph>
    <Paragraph position="1"> The IBM MASTOR speech-to-speech translation system has been developed for the DARPA CAST and Transtac programs whose mission is to develop technologies that enable rapid deployment of real-time S2S translation of low-resource languages on portable devices. It originated from the IBM MARS S2S system handling the air travel reservation domain described in [1], which was later significantly improved in all components, including ASR, MT and TTS, and later evolved into the MASTOR multi-lingual S2S system that covers much broader domains such as medical treatment and force protection [2,3]. More recently, we have further broadened our experience and efforts to very rapidly develop systems for under-studied languages, such as regional dialects of Arabic. The intent of this program is to provide language support to military, medical and humanitarian personnel during operations in foreign territories, by deciphering possibly critical language communications with a two-way real-time speech-to-speech translation system designed for specific tasks such as medical triage and force protection.</Paragraph>
    <Paragraph position="2"> The initial data collection effort for the project has shown that the domain of force protection and medical triage is, though limited, rather broad. In fact, the definition of domain coverage is tough when the speech from responding foreign language speakers are concerned, as their responses are less constrained and may include out-of-domain words and concepts. Moreover, flexible casual or colloquial speaking style inevitably appears in the human-to-human conversational communications. Therefore, the project is a great challenge that calls for major research efforts.</Paragraph>
    <Paragraph position="3"> Among all the challenges for speech recognition and translation for under-studied languages, there are two main issues: 1) Lack of appropriate amount of speech data that represent the domain of interest and the oral language spoken by the target speakers, resulting in difficulties in accurate estimation of statistical models for speech recognition and translation. 2) Lack of linguistic knowledge realization in spelling standards, transcriptions, lexicons and dictionaries, or annotated corpora. Therefore, various different approaches have to be explored.</Paragraph>
    <Paragraph position="4"> Another critical challenge is to embed complicated algorithms and programs into small devices for mobile users. A hand-held computing device may have a CPU of 256MHz and 64MB memory; to fit the programs, as well as the models and data files into this memory and operate the system in real-time are tremendous challenges [4].</Paragraph>
    <Paragraph position="5"> In this paper, we will describe the overall framework of the MASTOR system and our approaches for each major component, i.e., speech recognition and translation. Various statistical approaches [5,6,7,8] are explored and used to solve different technical challenges. We will show how we addressed the challenges that arise when building automatic speech recognition (ASR) and machine translation (MT) for colloquial Arabic on both the laptop and handheld PDA platforms.</Paragraph>
  </Section>
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