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Automatic pronunciation evaluation of foreign speakers using unknown text

机译：使用未知文本自动评估外语发音

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In this study we present various techniques to evaluate the pronunciation of students of a foreign language without any knowledge of the uttered text. Previous attempts have shown that it is feasible to evaluate the pronunciation of a non-native speaker by having implicit or explicit knowledge of the uttered text, provided that enough utterances are available. Our approach is to use characteristics of the mother tongue (SOURCE language) of the speaker in the evaluation of his/ her pronunciation. We recorded 20 Greek students speaking English (TARGET language) and evaluated their pronunciation using algorithms that include characteristics of the SOURCE language (Greek). We show that the pronunciation scores that are based on both TARGET- and SOURCE-language characteristics have better correlation with the human scores than those based only on characteristics of the TARGET language. As in previous studies, we found that the best-performing algorithms for automatic evaluation of pronunciation are based on speech recognition technology.
机译：在这项研究中，我们提出了各种技术来评估外语学生的发音，而无需对所讲出的文字有任何了解。先前的尝试表明，只要有足够的语音可用，通过对语音文本具有隐式或显性知识来评估非母语说话者的发音是可行的。我们的方法是在评估发音时使用说话者的母语（SOURCE语言）的特征。我们记录了20名希腊学生说英语（目标语言），并使用包括源语言（希腊语）特征的算法评估了他们的发音。我们表明，与仅基于TARGET语言的特征相比，基于TARGET和SOURCE语言特征的发音分数与人类分数具有更好的相关性。与以前的研究一样，我们发现用于语音自动评估的最佳算法是基于语音识别技术的。

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