Faculty Profile
 
  DAI lirong
     
Department: Department of Electronic Engineering & Information Science
Mailing Address:
Department of Electronic Engineering & Information Science, University of Science and Technology of China, 443 Huangshan Rd, Hefei, Anhui, PR China
Postal Code:
230027
Phone:
+86-551-63603645
Fax:
Homepage:
http://eeis.ustc.edu.cn/szdw/bd/dlr/201008/t20100825_32023.html
 
       

Research Profile

Dai Lirong, male, born in July 1962, is a professor at the Department of Electronic Engineering and Information Science, University of Science and Technology of China (USTC). Dai is mainly interested in the research on speech recognition, speech synthesis, content-based audio retrieval, real-time DSP (Digital Signal Processing) technology, adaptive signal processing, etc. Prof. Dai is also the chief instructor of the course of “Modern Digital Signal Processing” to graduate-level students.
     Prof. Dai received his B.E. in Electronic Engineering from the Department of Electronic Engineering, Xidian University in July 1983, his M.E. in Signal and Systems Engineering from the Department Computer Science and Electronic Engineering, Hefei University of Technology in July 1986, and his Ph.D. in Signal and Information Processing from the Department of Electronic Engineering and Information Science, University of Science and Technology of China in July 1997. From July 1986 to February 1994, Dai worked as an assistant teacher and lecturer successively at the Department Computer Science and Electronic Engineering, Hefei University of Technology. In July 1997, Dai joined the Faculty of USTC and has been working in succession as a lecturer, associate professor and full professor at the Department of Electronic Engineering and Information Science since.
 
     
Selected Publications
1) HMM-based Unit Selection Speech Synthesis Using Log Likelihood Ratios Derived from Perceptual Data , Speech Communication , 2014 , vol. 63-64
2) Unsupervised Prosodic Labeling of Speech Synthesis Databases Using Context-Dependent HMMs , IEICE Transactions on Information and Systems , 2014 , vol.E97-D, no.6
3) Improving deep neural networks for LVCSR using dropout and shrinking structure , ICASSP. FLORENCE, ITALY:IEEE , 2014.5 , ICASSP-2014
4) Direct Adaptation of Hybrid DNN/HMM Model for Fast Speaker Adaptation , ICASSP. FLORENCE, ITALY:IEEE , 2014.5 , ICASSP-2014
5) An Experimental Study on Speech Enhancement Based on Deep Neural Networks , IEEE SIGNAL PROCESSING LETTERS , 2014.1 , VOL. 21, NO. 1
6) Minimum Divergence Estimation of Speaker Prior in Multi-Session PLDA , ICASSP.FLORENCE, ITALY:IEEE , 2014.5 , ICASSP-2014
7) 深度语音信号与信息处理:研究进展与展望 , 数据采集与处理 , 2014.3 , VOL.29,NO.2
8) Deep Bottleneck Features for Spoken Language Identification , PLoS One , 2014.7 , 2014 Jul 1;9(7)
9) i-vector representation based on bottleneck features for language identification , Electronics Letters , 2013 , Vol.49, no. 24
10) Minimum Kullback-Leibler Divergence Parameter Generation for HMM-based Speech Synthesis , IEEE TRANS. ON ASLP , 2012.7 , Volume:20 , Issue: 5
11) Trust Region-Based Optimization for Maximum Mutual Information Estimation of HMMs in Speech Recognition , IEEE. Trans. on ASLP , 2011 , VOL. 19, NO. 8
 
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