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

Chao Wang, S. Seneff
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引用次数: 12

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.
用于计算机辅助语言学习的高质量语音到语音翻译
本文描述了我们在口语翻译方面的研究,旨在将计算机辅助工具应用于第二语言习得。翻译框架被整合到一个多语言对话系统中,学生可以用外语与系统进行自然的口头互动,同时随时用母语提出问题,以获得口头翻译的语言帮助。因此,翻译的质量必须非常高,但领域是有限的。以英语为母语的人学习普通话为场景,在天气信息域进行了实验。我们能够利用大量的英语天气域查询语料库来探索和比较各种翻译策略:正式的、基于示例的和统计的。在695个自发话语的测试集上手动评估翻译质量。最佳的语音翻译性能(89.9%正确,6.1%不正确,4.0%被拒绝)是由一个系统实现的,该系统结合了形式和基于示例的方法,使用特定领域的中文语法的可解析性作为拒绝标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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