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2015年7月16日 (四) 07:23
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张之勇 2014-12-28 APSIPA paper reading
刘超2015-03-11 Towards End-to-End Speech Recognition with Recurrent Neural Networks
汤志远2015-3-18 - Building DNN Acoustic Models for Large Vocabulary Speech Recognition
林一叶2015-4-1 - CONTRASTIVE AUTO-ENCODER FOR PHONEME RECOGNITION
张雪薇2015-4-1 - speech dereverberation using weighted prediction error with laplacian model of the designed signal
王东2015-4-1 - Do Deep Nets Really Need to be Deep?
王东2015-4-1 - Distilling the Knowledge in a Neural Network
殷实2015-4-8 - Neural_Network_Acoustic_Models_with_Superviesed_Hidden_Layers_for_Automatic_Speech_Recognition
赵梦原2015-4-8 - Ensemble_Deep_Learning_for_Speech_Recognition
曾翔宇2015-3-11 - An evaluation of target speech for a nonaudible murmur enhancement system in noisy enviroment
刘超2015-04-14 Real-time one-pass decoding with recurrent neural network language model for speech recognition
汤志远 2015-04-29 Reshaping deep neural network for fast decoding by node-pruning
张雪薇2015-4-29 SPEECH DEREVERBERATION WITH MULTI-CHANNEL LINEAR PREDICTION AND SPARSE PRIORS FOR THE DESIRED SIGNAL
张雪薇2015-4-29 MULTI-CHANNEL LINEAR PREDICTION-BASED SPEECH DEREVERBERATION WITH LOW-RANK POWER SPECTROGRAM APPROXIMATION
张之勇2015-5-6 On the importance of initialization and momentum in deep learning.pdf
张雪薇2015-5-20 ICASSP_dereveration
汤志远2015-5-27 ICASSP selected readings
张之勇2015-6-24 2015_Submodular data selection with acoustic and phonetic features for automatic speech recognition.pdf
张雪薇2015-7-1 SPEECH DEREVERBERATION USING A LEARNED SPEECH MODEL.pdf
汤志远2015-7-8 Deep Recurrent Neural Networks for Acoustic Modelling
汤志远2015-7-8 LSTM: A Search Space Odyssey
汤志远2015-7-8 Grid Long Short-Term Memory
汤志远2015-7-8 Visualizing and Understanding Recurrent Networks
赵梦原2015-7-8 Annealed Dropout_trained Maxout Networks for Improved LVCSR
赵梦原2015-7-8 Automatic Pronunciation Verification for Speech Recognition
张雪薇2015-7-16 2015_Frame-by-frame_language_identification_in_short_utterances_using_deep_neural_networks
张雪薇2015-7-16 SEQUENCE_CLASSIFICATION_USING_THE_HIGH-LEVEL_FEATURES_EXTRACTED
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