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Improvements in predicting children's overall reading ability by modeling variability in evaluators' subjective judgments

机译:通过对评估者主观判断中的变异性进行建模来改善预测儿童的整体阅读能力

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Automatic literacy assessment is one promising application of speech and language processing research. In our previous work, we showed we could accurately predict children''s overall ability to read a list of English words aloud, an integral component of early literacy assessment. In this paper, we improve upon our results by exploiting the fact that evaluators'' level of agreement significantly varies, depending on the child being judged. This source of evaluator variability is directly modeled using generalized least squares linear regression. In this framework, the children for which the evaluators were more confident in rating are weighted higher. Performance in predicting the mean evaluator''s scores increases from a Pearson''s correlation coefficient of 0.946 to 0.952, a relative improvement of 0.63%. This is a significantly higher correlation than the mean inter-evaluator agreement of 0.899 (p < 0.05). Critically, the mean and maximum absolute errors are significantly reduced.
机译:自动扫盲评估是语音和语言处理研究的一种有前途的应用。在我们以前的工作中,我们证明了我们可以准确预测孩子大声朗读英语单词列表的整体能力,这是早期识字评估的一个组成部分。在本文中,我们利用评估者的同意水平显着不同的事实来改善我们的结果,具体取决于被评判的孩子。使用广义最小二乘线性回归直接对评估者变异性的来源进行建模。在此框架中,评估者对评分更有信心的孩子的权重更高。预测平均评估者得分的性能从Pearson的相关系数从0.946提高到0.952,相对提高了0.63%。与0.899的评估员之间的平均协议一致性(p <0.05)相比,这具有显着更高的相关性。至关重要的是,平均误差和最大绝对误差显着降低。

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