In this study, multidimensional feature extraction is performed on the U-language recordings of the test takers, and these features are evaluated separately, with five categories of features: pronunciation, fluency, vocabulary, grammar, and semantics. A deep neural network model is constructed to model the feature values to obtain the final score. Based on the previous research, this study uses a deep neural network training model instead of linear regression to improve the correlation between model score and expert score. The method of using word frequency for semantic scoring is replaced by the LDA topic model for semantic analysis, which eliminates the need for experts to manually label keywords before scoring and truly automates the critique. Also, this paper introduces text cleaning after speech recognition and deep learning-based speech noise reduction technology in the scoring model, which improves the accuracy of speech recognition and the overall accuracy of the scoring model. Also, innovative applications and improvements are made to key technologies, and the latest technical solutions are integrated and improved. A new open oral grading model is proposed and implemented, and innovations are made in the method of speech feature extraction to improve the dimensionality of open oral grading.

中文翻译：

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基于多特征融合算法的口语测试中的发音错误检测

在这项研究中，对考生的U语言记录执行多维特征提取，并对这些特征进行单独评估，包括五类特征：发音，流利性，词汇量，语法和语义。构建了深度神经网络模型以对特征值建模以获得最终分数。在先前研究的基础上，本研究使用深度神经网络训练模型代替线性回归，以改善模型评分与专家评分之间的相关性。使用词频进行语义评分的方法已被用于语义分析的LDA主题模型所取代，这消除了专家在评分之前手动标记关键字的需求，并真正实现了评论的自动化。还，本文在评分模型中介绍了语音识别后的文本清理和基于深度学习的语音降噪技术，从而提高了语音识别的准确性和评分模型的整体准确性。此外，还对关键技术进行了创新性的应用和改进，并对最新的技术解决方案进行了集成和改进。提出并实现了一种新的开放式口语评分模型，对语音特征提取方法进行了创新，提高了开放式口语评分的维度。