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- W2797789629 abstract "In this paper, we presented an effective classifier for foreign accented spoken English to determine the origins of the speaker. We created an accented spoken English corpus that consists of 30 speakers from 6 different countries including China, India, France, Germany, Turkey and Spain. We used MFCC as feature and Deep belief network (DBN) as classifier. The parameters of the DBN were determined by an iterative approach in which the node weight is updated according to the substitution error. Our method achieved an accuracy of 90.2% for 2 accented datasets and 71.9% for 6 accented datasets. The result is much better than the other state-of-art methods such as SVM, k-NN and random forest which have around 40% accuracy." @default.
- W2797789629 created "2018-04-24" @default.
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- W2797789629 date "2018-01-01" @default.
- W2797789629 modified "2023-10-02" @default.
- W2797789629 title "Foreign English Accent Classification Using Deep Belief Networks" @default.
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- W2797789629 doi "https://doi.org/10.1109/icsc.2018.00053" @default.
- W2797789629 hasPublicationYear "2018" @default.
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