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DNN Training Based on Classic Gain Function for Single-channel Speech Enhancement and Recognition.

, , and . ICASSP, page 910-914. IEEE, (2019)

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A Hybrid Approach to Combining Conventional and Deep Learning Techniques for Single-Channel Speech Enhancement and Recognition., , , and . ICASSP, page 2531-2535. IEEE, (2018)An iterative mask estimation approach to deep learning based multi-channel speech recognition., , , , , , and . Speech Commun., (2019)A Dilated Transformer Network for Time Series Anomaly Detection., , , and . ICTAI, page 48-52. IEEE, (2022)LSTM-based iterative mask estimation and post-processing for multi-channel speech enhancement., , , and . APSIPA, page 488-491. IEEE, (2017)DNN Training Based on Classic Gain Function for Single-channel Speech Enhancement and Recognition., , and . ICASSP, page 910-914. IEEE, (2019)A speaker-dependent deep learning approach to joint speech separation and acoustic modeling for multi-talker automatic speech recognition., , , and . ISCSLP, page 1-5. IEEE, (2016)Attention Based Fully Convolutional Network for Speech Emotion Recognition., , , , and . APSIPA, page 1771-1775. IEEE, (2018)On Design of Robust Deep Models for CHiME-4 Multi-Channel Speech Recognition with Multiple Configurations of Array Microphones., , , , and . INTERSPEECH, page 394-398. ISCA, (2017)Speech Separation based on signal-noise-dependent deep neural networks for robust speech recognition., , , and . ICASSP, page 61-65. IEEE, (2015)2D-to-2D Mask Estimation for Speech Enhancement Based on Fully Convolutional Neural Network., , and . ICASSP, page 6664-6668. IEEE, (2020)