The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter ...
ISBN:
(数字)9781728145815
ISBN:
(纸本)9781728145822
The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter the nature of the word. In this study, we proposed an automated language scoring system to test the proficiency of Chinese language. We have employed a novel fusion approach of a 38-feature based model and a Siamese convolutional neural network (Siamese CNN) which can accuracy identify the difference between the native speech and the test taker's speech. The results show that out model have achieved comparable performance to the state of the art and solved the pronunciation problems as well. Furthermore, we have provided a fusion based approach and provided extensive amount of experiments which shows that our method is state of the art and can be utilized in real time Chinese language proficiency scoring.
In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cell...
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Event structures are one of the best known models for concurrency. Many variants of the basic model and many possible notions of equivalence for them have been devised in the literature. In this paper, we study how th...
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Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets ...
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Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets labeling, which in turn makes evaluations of note onset detection algorithms difficult to compare. This paper gives an overview of the parameters influencing the commonly used onset detection evaluation measure, i.e. the F1-score, pointing out a consistently missing parameter which is the overall time shift in annotations. This paper shows how crucial this parameter is in making reported F1-scores comparable among different algorithms and datasets, achieving a more reliable evaluation. As several MIR applications are concerned with the relative location of onsets to each other and not their absolute location, this paper suggests to include the overall time shift as a parameter when evaluating the algorithm performance. Experiments show a strong variability in the reported F1-score and up to 50% increase in the best-case F1-score when varying the overall time shift. Optimizing the time shift turns out to be crucial when training or testing algorithms with datasets that are annotated differently (e.g. manually, automatically, and with different annotators) and especially when using deep learning algorithms.
This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitiv...
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Present approaches of automated language scoring lack the ability to investigate the multiple-level and several contexts of sequential features which are helpful to examine the language proficiency for the responses (...
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Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart healthcare systems are some examples of these applications, all of which ar...
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Punctuation is a strong indicator of syntactic structure, and parsers trained on text with punctuation often rely heavily on this signal. Punctuation is a diversion, however, since human language processing does not r...
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Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly st...
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We present graph-theoretic characterisations of three notions of inefficiency arising in network models: edge-weakness in flow networks, node-weakness in depletable channels, and vulnerability in traffic networks. Our...
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