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Raw waveform-based speech enhancement by fully convolutional networks.

, , , and . APSIPA, page 6-12. IEEE, (2017)

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Dimension reduction for speaker identification based on mutual information., and . INTERSPEECH, page 2021-2024. ISCA, (2007)Speech spectrum restoration based on conditional restricted boltzmann machine., , and . INTERSPEECH, page 3259-3263. ISCA, (2013)Study of articulators' contribution and compensation during speech by articulatory speech recognition., , , , , , and . Multimedia Tools Appl., 77 (14): 18849-18864 (2018)Speech restoration based on deep learning autoencoder with layer-wised pretraining., , , and . INTERSPEECH, page 1504-1507. ISCA, (2012)Ensemble speaker modeling using speaker adaptive training deep neural network for speaker adaptation., , , and . INTERSPEECH, page 2892-2896. ISCA, (2015)Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers., , , , , , and . IUKM, volume 11471 of Lecture Notes in Computer Science, page 295-307. Springer, (2019)Incremental training and constructing the very deep convolutional residual network acoustic models., , , , , and . ASRU, page 222-227. IEEE, (2017)F0 Contour Analysis Based on Empirical Mode Decomposition for DNN Acoustic Modeling in Mandarin Speech Recognition., , , and . INTERSPEECH, page 973-977. ISCA, (2016)Deep progressive multi-scale attention for acoustic event classification., , , , and . CoRR, (2019)Improving CTC-based Acoustic Model with Very Deep Residual Time-delay Neural Networks., , , , , and . INTERSPEECH, page 3708-3712. ISCA, (2018)