中国科学技术大学 dailirong--Home--Paper Publications

戴礼荣

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Paper Publications

[1] Cong Liu, Yu Hu, Li-Rong Dai, and Hui Jiang ,Trust Region-Based Optimization for Maximum Mutual Information Estimation of HMMs in Speech Recognition, IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 19, NO. 8, NOVEMBER 2011, pp.2474 – 2485.
[2] Zhen-Hua Ling, Li-Rong Dai, Minimum Kullback-Leibler Divergence Parameter Generation for HMM-based Speech Synthesis, IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING，1492-1502，July 2012，Volume:20 , Issue: 5.
[3] Song, Yan, Bing Jiang, YeBo Bao, Si Wei, and Li-Rong Dai. ，i-vector representation based on bottleneck features for language identification. Electronics Letters Vol.49, no. 24 (2013): 1569-1570..
[4] Bing, Jiang, Yan Song, Si Wei, Jun-Hua Liu, Ian Vince McLoughlin and Li-Rong Dai. Deep Bottleneck Features for Spoken Language Identification. PLoS One. 2014 Jul 1;9(7):e100795. doi: 10.1371.
[5] 戴礼荣，张仕良，深度语音信号与信息处理：研究进展与展望，2014.3，数据采集与处理，VOL.29，NO.2，171-179.
[6] Yong Xu、Jun Du、Li-Rong Dai、Chin-Hui LEE ，An Experimental Study on Speech Enhancement Based on Deep Neural Networks 、2014.01、 IEEE SIGNAL PROCESSING LETTERS、VOL. 21, NO. 1、65-68.
[7] Liping Chen, Kong-Aik Lee, Bin Ma, Wu Guo, Haizhou Li, Li-Rong Dai, Minimum Divergence Estimation of Speaker Prior in Multi-Session PLDA, ICASSP. FLORENCE, ITALY:IEEE, MAY 4-9, 2014.
[8] Shaofei Xue, Ossama Abdel-Hamid, Hui Jiang, Li-Rong Dai, Direct Adaptation of Hybrid DNN/HMM Model for Fast Speaker Adaptation, ICASSP. FLORENCE, ITALY:IEEE, MAY 4-9, 2014.
[9] Shiliang Zhang, Yebo Bao, Pan Zhou, Hui Jiang, Li-Rong Dai, Improving deep neural networks for LVCSR using dropout and shrinking structure, ICASSP. FLORENCE, ITALY:IEEE, MAY 4-9, 2014.
[10] Chen-Yu Yang, Zhen-Hua Ling, and Li-Rong Dai, "Unsupervised Prosodic Labeling of Speech Synthesis Databases Using Context-Dependent HMMs", IEICE Transactions on Information and Systems, vol.E97-D, no.6, pp. 1449-1460, 2014..

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