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An efficient automated answer scoring system for Punjabi language
Egyptian Informatics Journal ( IF 5.2 ) Pub Date : 2018-11-29 , DOI: 10.1016/j.eij.2018.11.001
Tarandeep Singh Walia , Gurpreet Singh Josan , Amarpal Singh

Automated Scoring is a developing technology. The accuracy and reliability of these systems have been proven to be much higher. Besides being a time-and money-saver, there are a number of studies which are being conducted to provide variety in feedback, not only on grammatical issues, but also on semantic related issues. This will reduce not only the paper load of the teachers, but as well as teachers’ assessment related issues. In this paper, we remove those dummy note bins, thereby having 183 dimensions in total. We augmented the input by concatenating it with the first-order difference of the semitone filtered spectrogram. We observed a significant increase of the transcription performance with this addition. Compared to feedforward neural networks, recurrent neural network (RNN) are capable of learning temporal dependency of sequential data, which is the property found in music answer scoring.Also, the Long Short-Term Memory (LSTM) unit has a memory block updated only when an input or forget gate is open, and the gradients can propagate through memory cells without being multiplied each time step. Throughout backward and forward layers together, the networks can access to both history and future of the given time frame. Comparative analyses show that the proposed technique outperforms existing techniques.



中文翻译:

旁遮普语的高效自动答案评分系统

自动计分是一项发展中的技术。这些系统的准确性和可靠性已被证明要高得多。除了节省时间和金钱外,还进行了许多研究,以提供各种反馈,不仅涉及语法问题,还涉及语义相关问题。这不仅可以减轻教师的纸张负担,而且可以减少教师评估相关的问题。在本文中,我们删除了这些虚拟便笺纸槽,因此总共具有183个尺寸。我们通过将输入与半音滤波频谱图的一阶差分连接起来来扩充输入。我们观察到这种添加使转录性能显着提高。与前馈神经网络相比,递归神经网络(RNN)能够学习顺序数据的时间依赖性,此外,长短时记忆(LSTM)单元具有仅在输入或忘记门打开时更新的存储块,并且梯度可以在存储单元中传播而不会每次相乘步。在整个后向和前进层中,网络可以访问给定时间范围的历史记录和将来记录。比较分析表明,所提出的技术优于现有技术。

更新日期:2018-11-29
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