ITR/PE: AVENUE: Adaptable Voice Translation for Minority Languages

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Our primary research goal is to develop a prototype voice-enabledtranslating communicator which will deliver information services acrossthe linguistic divide for minority languages in order allow remotelinguistically-diverse users to communicate directly with Internetcontent and databases, and more importantly to communicate with othersspeaking a different language from their own. The latter will enableinformation, education, and, for example, health services, to reachremote minority-language communities. Achieving this goal requiresmajor advances in machine learning for translation and in cross-languagespeech-recognition adaptability to wider language phenomena.Traditional transfer-rule-based MT requires up to a person-centuryto build and perfect a new language pair. Statistical andExample-Based MT replaces human coding effort by vast amounts ofbilingual training data, which are virtually unobtainable for mostminority languages. Without a radical advance, leading to anover-an-order-of-magnitude improvement in development time, theonly commercially justifiable MT applications involve the majorEuropean languages, Japanese, Chinese, Korean, Arabic and perhaps acouple more relatively-popular languages. The vast majority of humanlanguages are currently relegated to the proverbial MT dust heap.We propose new MT approaches based on extended and new machinelearning methods. The first approach consists of statistical MTmethods that learn from orders of magnitude less training data,and that can more effectively incorporate prior linguistic information(including dictionaries, word classes, and known linguisticrule classes or constraints) by using the joint source-channelmodeling approach combined with exponential (maximum entropy) models.The second approach is a new method for acquiring high-quality MTtransfer rules from native informants which decreases dependence onhuman experts and reduces development time. Semantically-conditionedtransfer rules are generalized via a new locally-constrainedSeeded Version-Space method based on a controlled bilingual corpusand interactive tools to elicit information from native informants.The third method builds general phone models across multiple languagefamilies for speech recognition and adapts the recognizer to newlanguages with minimal new- language training data. All of thesemethods are based on new and existing machine learning algorithmsthat combine prior knowledge with limited amounts of new data inorder to converge quickly on working machine translation and speechrecognition and synthesis systems.The primary societal impact will be a significant contributionto the global democratization of informa- tion, a process thatrequires bridging current linguistic barriers, especially forlow-density or economically- disadvantaged languages. Additionally,preservation and teaching of endangered languages will be directlyenabled by the new linguistic and acoustic knowledge coupled withexisting tutorial software. If successful, Avenue (Adaptable Voice-EnabledNatural-translator for Universal Empowerment) will be the prototypeof an MT system that will empower world-wide access to multilingualinformation.