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Deep Learning Publications:

1. Xiaoxu Li, Dongliang Chang, Zhanyu Ma, Zheng-Hua Tan, Jing-Hao Xue, Jie Cao, Jingyi Yu, Jun Guo, “OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer,” accepted by IEEE Transactions on Image Processing, 2020.
   
2. Iván López-Espejo, Zheng-Hua Tan and Jesper Jensen, “Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices,” accepted by IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020.
   
3. Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen and Jesper Jensen, “On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 28, pp. 825-838, January 2020.
   
4. Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson and Jesper Jensen, “Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect,” Speech Communication, vol. 115, pp. 38-50, December 2019.
   
5. Miklas S. Kristoffersen, Sven E. Shepstone, and Zheng-Hua Tan, “The Importance of Context When Recommending TV Content: Dataset and Algorithms,” accepted by IEEE Transactions on Multimedia, 2019.
   
6. Yonggang Qi and Zheng-Hua Tan, "SketchSegNet+: An End-to-end Learning of RNN for Multi-Class Sketch Semantic Segmentation," IEEE Access, vol. 7, pp. 102717-102726, July 2019.
   
7. Achintya kr. Sarkar, Zheng-Hua Tan, Hao Tang, Suwon Shon and James Glass, "Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification" accepted by IEEE Transactions on Audio, Speech and Language Processing.
8. Saeid Samizade, Zheng-Hua Tan, Chao Shen, Xiaohong Guan, "Adversarial Example Detection by Classification for Deep Speech Recognition,” The 45th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), Barcelona, May 4-8, 2020.
   
9. Jiyang Xie, Zhanyu Ma, Guoqiang Zhang, Jing-Hao Xue, Zheng-Hua Tan and Jun Guo, “Soft Dropout and Its Variational Bayes Approximation,” 2019 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2019), Oct. 13–16, 2019, Pittsburgh, PA, USA.
10. Iván López-Espejo, Zheng-Hua Tan, and Jesper Jensen, "Keyword Spotting for Hearing Assistive Devices Robust to External Speakers," Interspeech 2019, September 15-19, 2019, Graz, Austria.
    
11. Miklas S. Kristoffersen, Jacob L. Wieland, Sven E. Shepstone, Zheng-Hua Tan and Vinoba Vinayagamoorthy, “Deep Joint Embeddings of Context and Content for Recommendation,” CARS 2.0 – Workshop on Context-Aware Recommender Systems, in conjunction with RecSys’ 2019, 20 September 2019, Copenhagen, Denmark.
    
12. Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson and Jesper Jensen, “Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems,” 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 12-17, 2019.
13. Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson and Jesper Jensen, “On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement,” 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, May 12-17, 2019.
    
14. Andrea Coifman, Peter Rohoska, Miklas S. Kristoffersen, Sven E. Shepstone, and Zheng-Hua Tan, "Subjective Annotations for Vision-Based Attention Level Estimation," The 14th International Conference on Computer Vision Theory and Applications (VISAPP 2019), Prague, Czech Republic, 25-27 February 2019.
    
15. Morten Kolbæk, Zheng-Hua Tan and Jesper Jensen, "On the Relationship between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement," IEEE Transactions on Audio, Speech and Language Processing, vol. 27, no. 2, pp. 283-295, February 2019.
    
16. Hong Yu, Zheng-Hua Tan, Zhanyu Ma, Rainer Martin, and Jun Guo, "Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp 4633-4644, October 2018.
    
17. Asger Heidemann Andersen, Jan Mark de Haan, Zheng-Hua Tan and Jesper Jensen, "Non-Intrusive Speech Intelligibility Prediction using Convolutional Neural Networks," IEEE Transactions on Audio, Speech and Language Processing, vol. 26, no. 10, pp. 1925-1939, October 2018.
18. Hong Yu, Tianrui Hu, Zhanyu Ma, Zheng-Hua Tan and Jun Guo, "Multi-Task Adversarial Network Bottleneck Features for Noise-Robust Speaker Verification," the IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2018), Guiyang, China, August 22 - 24, 2018.
19. Peter Sibbern Frederiksen, Jesus Villalba, Shinji Watanabe, Zheng-Hua Tan and Najim Dehak, "Effectiveness of Single-Channel BLSTM Enhancement for Language Identification," Interspeech 2018, Hyderabad, India, September 2-6, 2018.
20. Morten Kolbæk, Dong Yu, Zheng-Hua Tan and Jesper Jensen, "Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks”, IEEE Transactions on Audio, Speech and Language Processing, vol. 25, no. 10, October 2017, pp. 1901-1913.
21. Dong Yu, Morten Kolbæk, Zheng-Hua Tan, and Jesper Jensen, “Permutation Invariant Training of Deep Models for Speaker-independent Multi-talker Speech Separation,” The 42th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, USA, 5-9 March 2017.
22. Morten Kolbæk, Dong Yu, Zheng-Hua Tan and Jensen, Jesper, "Joint Separation and Denoising of Noisy Multi-Talker Speech Using Recurrent Neural Networks and Permutation Invariant Training,” the IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan, 25-28 September 2017. Best student paper award.
23. Daniel Michelsanti and Zheng-Hua Tan, "Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification,” Interspeech 2017, Stockholm, Sweden, 20-24 August 2017.
24. Hong Yu, Zheng-Hua Tan, Zhanyu Ma and Jun Guo, "Adversarial Network Bottleneck Features for Noise Robust Speaker Verification,” Interspeech 2017, Stockholm, Sweden, 20-24 August 2017.
25. Morten Kolbæk, Zheng-Hua Tan and Jesper Jensen, "Speech Intelligibility Potential of General and Specialized Deep Neural Network based Speech Enhancement Systems," IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 25, no. 1, pp. 153-167, January 2017.
26. Morten Kolbæk, Zheng-Hua Tan and Jesper Jensen, “Monaural Speech Enhancement Using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure,” The 43th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), 15-20 April 2018, Calgary, Alberta, Canada.
27. Hong Yu, Zheng-Hua Tan, Yiming Zhang, Zhanyu Ma, and Jun Guo, “DNN Filter Bank Cepstral Coefficients for Spoofing Detection,” IEEE Access, vol. 5, pp. 4779-4787, March 2017. PDF from IEEEXplore. Filter bank neural networks (FBNN.zip, 55 MB)
28. A.K. Sarkar, Z.-H. Tan, "Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification," NIPS 2017 Time Series Workshop, Long Beach, CA, USA, Dec. 8, 2017.
29. Morten Kolbæk, Zheng-Hua Tan and Jesper Jensen, "Speech Enhancement Using Long Short-Term Memory Based Recurrent Neural Networks for Noise Robust Speaker Verification,” 2016 IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA, December 13-16, 2016.