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Semi-Supervised Training in Deep Learning Acoustic Model
2016
Interspeech 2016
We studied the semi-supervised training in a fully connected deep neural network (DNN), unfolded recurrent neural network (RNN), and long short-term memory recurrent neural network (LSTM-RNN) with respect to the transcription quality, the importance data sampling, and the training data amount. We found that DNN, unfolded RNN, and LSTM-RNN are increasingly more sensitive to labeling errors. For example, with the simulated erroneous training transcription at 5%, 10%, or 15% word error rate (WER)
doi:10.21437/interspeech.2016-1596
dblp:conf/interspeech/HuangWG16
fatcat:tj6j6oxy7nh4llidsaapbxcmkq