DNN-Based Scoring of Language Learners’ Proficiency Using Learners’ Shadowings and Native Listeners’ Responsive Shadowings
DNN-Based Scoring of Language Learners’ Proficiency Using Learners’ Shadowings and Native Listeners’ Responsive Shadowings
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DOI：
10.1109/slt.2018.8639645
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发表时间：
2018-12
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影响因子：
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通讯作者：
Suguru Kabashima;Y. Inoue;D. Saito;N. Minematsu
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文献类型：
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作者：
Suguru Kabashima;Y. Inoue;D. Saito;N. Minematsu
本文研究了基于DNN的评分技术，当它们被应用到两个任务相关的外语教育。一种是传统的任务，它试图预测语言学习者的口语交际能力。为此，学习者的阴影话语自动评估。另一个是一个非常新的和新颖的任务，试图预测学习者的发音的可理解性或可理解性。在这项任务中，本机听众的反应阴影进行评估。对于这两个任务，测试了类似的技术框架，其中基于DNN的音素后验子，基于后验图的DTW分数，基于ASR的准确度，阴影延迟等用于训练回归模型，其目的是预测手动评分。实验表明，在这两个任务中，基于DNN的预测分数与平均人类分数之间的相关性高于或至少与人类评分员分数之间的平均相关性相当。这一事实清楚地表明，我们提出的自动评分模块可以引入语言教育作为另一个人的评分。
This paper investigates DNN-based scoring techniques when they are applied to two tasks related to foreign language education. One is a conventional task, which attempts to predict a language learner’s overall proficiency of oral communication. For this aim, learners’ shadowing utterances are assessed automatically. The other is a very new and novel task, which attempts to predict intelligibility or comprehensibility of a learner’s pronunciation. In this task, native listeners’ responsive shadowings are assessed. For both the tasks, similar technical frameworks are tested, where DNN-based phoneme posteriors, posteriogram-based DTW scores, ASR-based accuracies, shadowing latencies, etc are used to train regression models, which aim to predict manually rated scores. Experiments show that, in both the tasks, the correlation between the DNN-based predicted scores and the averaged human scores is higher than or at least comparable to the averaged correlation between the scores of human raters. This fact clearly indicates that our proposed automatic rating module can be introduced to language education as another human rater.