High-quality speech-to-speech translation for computer-aided language learning

Abstract

This article describes our research on spoken language translation aimed toward the application of computer aids for second language acquisition. The translation framework is incorporated into a multilingual dialogue system in which a student is able to engage in natural spoken interaction with the system in the foreign language, while speaking a query in their native tongue at any time to obtain a spoken translation for language assistance. Thus the quality of the translation must be extremely high, but the domain is restricted. Experiments were conducted in the weather information domain with the scenario of a native English speaker learning Mandarin Chinese. We were able to utilize a large corpus of English weather-domain queries to explore and compare a variety of translation strategies: formal, example-based, and statistical. Translation quality was manually evaluated on a test set of 695 spontaneous utterances. The best speech translation performance (89.9% correct, 6.1% incorrect, and 4.0% rejected), is achieved by a system which combines the formal and example-based methods, using parsability by a domain-specific Chinese grammar as a rejection criterion.