IBM MASTOR SYSTEM: Multilingual Automatic Speech-to-speech Translator

Abstract

In this paper, we describe the IBM MASTOR, a speech-to-speech translation system that can translate spontaneous free-form speech in real-time on both laptop and hand-held PDAs. Challenges include speech recognition and machine translation in adverse environments, lack of training data and linguistic resources for under-studied languages, and the need to rapidly develop capabilities for new languages. Another challenge is designing algorithms and building models in a scalable manner to perform well even on memory and CPU deficient hand-held computers. We describe our approaches, experience, and success in building working free-form S2S systems that can handle two language pairs (including a low-resource language).

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA456612

Entities

People

  • Bowen Zhou
  • Charles Prosser
  • Hong-kwang Kuo
  • Liang Gu
  • Mohamed Afify
  • Ruhi Sarikaya
  • Wei Zhang
  • Wei-zhong Zhu
  • Yonggang Deng
  • Yuqing Gao

Organizations

  • IBM Thomas J. Watson Research Center

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Arabic Language
  • Automated Speech Recognition
  • Computational Science
  • Computer Languages
  • Computing Devices
  • Hidden Markov Models
  • Language
  • Linguistics
  • Machine Translation
  • Markov Models
  • Natural Language Understanding
  • Natural Languages
  • Probability
  • Recognition
  • Training
  • Translations

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Database Systems and Applications
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation