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Speech restoration based on deep learning autoencoder with layer-wised pretraining.

, , , and . INTERSPEECH, page 1504-1507. ISCA, (2012)

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Nonlinear processing in auditory system., and . NSIP, page 301-305. Bogaziçi University Printhouse, (1999)Semi-supervised ensemble DNN acoustic model training., , , , and . ICASSP, page 5270-5274. IEEE, (2017)Minimum Bayes risk training of CTC acoustic models in maximum a posteriori based decoding framework., , and . ICASSP, page 4855-4859. IEEE, (2017)Speaker Adaptive Training using Deep Neural Networks., , , , and . ICASSP, page 6349-6353. IEEE, (2014)Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders., , , , , , and . CoRR, (2020)Combination of multiple acoustic models with unsupervised adaptation for lecture speech transcription., , , , , , and . Speech Commun., (2016)Temporal contrast normalization and edge-preserved smoothing of temporal modulation structures of speech for robust speech recognition., , , and . Speech Commun., 52 (1): 1-11 (2010)Ensemble environment modeling using affine transform group., , , and . Speech Commun., (2015)Method of Estimating Signal-to-Noise Ratio Based on Optimal Design for Sub-band Voice Activity Detection., , , and . J. Inf. Hiding Multim. Signal Process., 8 (6): 1446-1459 (2017)Investigation of Semi-Supervised Acoustic Model Training Based on the Committee of Heterogeneous Neural Networks., , , and . INTERSPEECH, page 1325-1329. ISCA, (2016)