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- W1970870811 abstract "In this paper, we explore the use of deep belief network (DBN) posteriorgrams as input to our previously proposed comparison-based system for detecting word-level mispronunciation. The system works by aligning a nonnative utterance with at least one native utterance and extracting features that describe the degree of mis-alignment from the aligned path and the distance matrix. We report system performance under different DBN training scenarios: pre-training and fine-tuning with either native data only or both native and nonnative data. Experimental results have shown that by substituting the system input from MFCC or Gaussian posteriorgrams obtained in a fully unsupervised manner to DBN posteriorgrams, the system performance can be improved by at least 10.4% relatively. Moreover, the system performance remains steady when only 30% of the annotations being used." @default.
- W1970870811 created "2016-06-24" @default.
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- W1970870811 date "2013-05-01" @default.
- W1970870811 modified "2023-10-17" @default.
- W1970870811 title "Mispronunciation detection via dynamic time warping on deep belief network-based posteriorgrams" @default.
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- W1970870811 doi "https://doi.org/10.1109/icassp.2013.6639269" @default.
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