This paper presents an automatic proficiency assessment method for a non‐native Korean read utterance using bidirectional long short–term memory (BLSTM)–based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced‐alignment step using a native AM and non‐native AM, and (c) a linear regression–based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un‐segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

中文翻译：

---

使用双向基于LSTM的语音识别功能对非本地人朗读的韩语语音进行自动能力评估

本文介绍了一种基于双向长短期记忆（BLSTM）的声学模型（AM）和语音数据增强技术的非母语韩语发音的自动能力评估方法。具体而言，所提出的方法考虑了两种情况，带有和不带有提示文本。带有提示文本的建议方法执行（a）语音特征提取步骤，（b）使用本地AM和非本地AM的强制对齐步骤，以及（c）对这五个方法进行基于线性回归的熟练度评分步骤熟练程度分数。同时，在没有提示文本的情况下，所提出的方法还可以对遗漏的文本执行韩语语音识别和子词未分段的功能。实验结果表明，与采用常规AM的方法相比，所提出的带有提示文本的方法提高了所有分数的性能。另外，所提出的没有提示文本的方法的流利度得分性能与带有提示文本的方法相当。