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Automatic Detection of Common Mispronunciations of Vietnamese Speakers of English Using SVMs

机译：使用SVM自动检测越南语越南语的常见误用

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Pronunciation errors are often made by language learners. Especially, systematic mispronunciations, consisting of substitutions of native sounds for sounds of the target language that do not exist in the native language, are considered a big problem for language leaners. Therefore, automatic detection of this kind of errors is essential to building a Computer-Assisted Language Learning (CALL) system supporting language learners to improve their pronunciation. In this research, we focused on detecting systematic pronunciation errors made by Vietnamese learners of English. To this end, we used SVM classifiers, which are trained by a native corpuses (TIMIT) and a non-native corpus (V.E Corpus). The non-native corpus, constructed by the researchers and annotated by two Vietnamese trained professionals, includes 1550 utterances from 31 Vietnamese students. Each of the students was asked to read 50 English sentences designed to contain English phonemes frequently mispronounced by Vietnamese speakers. The experimental results showed that the detectors can achieve at least 79% SAR and 10% FAR.
机译：语音错误通常由语言学习者制作。特别是系统的误片，由母语中不存在的目标语言的声音的替代品组成，被认为是语言倾向者的大问题。因此，自动检测这种错误对于构建计算机辅助语言学习（呼叫）系统支持语言学习者来改进它们的发音至关重要。在这项研究中，我们专注于检测越南学习者英语学习者制作的系统发音错误。为此，我们使用了SVM分类器，其由本机语料（Timit）和非本机语料库（V.E语料库）培训。由研究人员构建并由两名越南训练有素的专业人士建造的非本机语料库包括来自31名越南学生的1550个话语。每个学生都被要求阅读50名旨在包含越南扬声器的英文音素的英语句子。实验结果表明，探测器可以达到至少79％的SAR和10％。

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