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NeuralDPS: Neural Deterministic Plus Stochastic Model With Multiband Excitation for Noise-Controllable Waveform Generation 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2022, 卷号: 30, 页码: 865-878
作者:  Wang, Tao;  Fu, Ruibo;  Yi, Jiangyan;  Tao, Jianhua;  Wen, Zhengqi
收藏  |  浏览/下载：227/0  |  提交时间：2022/06/06
Vocoders  Stochastic processes  Neural networks  Speech processing  Signal to noise ratio  Acoustics  Speech enhancement  Vocoder  speech synthesis  deterministic plus stochastic  multiband excitation  noise control  
Gated Recurrent Fusion With Joint Training Framework for Robust End-to-End Speech Recognition 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2021, 期号: 29, 页码: 198-209
作者:  Fan, Cunhang;  Yi, Jiangyan;  Tao, Jianhua;  Tian, Zhengkun;  Liu, Bin;  Wen, Zhengqi
Adobe PDF(2534Kb)  |  收藏  |  浏览/下载：344/47  |  提交时间：2021/03/08
Speech enhancement  Speech recognition  Training  Noise measurement  Logic gates  Acoustic distortion  Task analysis  Gated recurrent fusion  robust end-to-end speech recognition  speech distortion  speech enhancement  speech transformer  
Integrating Knowledge Into End-to-End Speech Recognition From External Text-Only Data 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2021, 卷号: 29, 页码: 1340-1351
作者:  Bai, Ye;  Yi, Jiangyan;  Tao, Jianhua;  Wen, Zhengqi;  Tian, Zhengkun;  Zhang, Shuai
收藏  |  浏览/下载：149/0  |  提交时间：2021/06/07
End-to-End  language modeling  speech recognition  teacher-student learning  transfer learning  
End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2020, 卷号: 28, 期号: 28, 页码: 1303-1314
作者:  Fan, Cunhang;  Tao, Jianhua;  Liu, Bin;  Yi, Jiangyan;  Wen, Zhengqi;  Liu, Xuefei
Adobe PDF(1344Kb)  |  收藏  |  浏览/下载：260/57  |  提交时间：2020/06/22
Feature extraction  Training  Interference  Speech enhancement  Clustering algorithms  Spectrogram  Speech separation  end-to-end post-filter  deep attention fusion features  deep clustering  permutation invariant training