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Deep Learning Publications:

  1. Asger Heidemann Andersen, Jan Mark de Haan, Zheng-Hua Tan and Jesper Jensen, "Non-Intrusive Speech Intelligibility Prediction using Convolutional Neural Networks,"accepted by IEEE Transactions on Audio, Speech and Language Processing.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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,” accepted byIEEE Transactions on Neural Networks and Learning Systems, 2017.
  11. Hong Yu, Zheng-Hua Tan, Yiming Zhang, Zhanyu Ma, and Jun Guo, “DNN Filter Bank Cepstral Coefficients for Spoofing Detection," accepted by IEEE Access. PDF from IEEEXplore. Filter bank neural networks (FBNN.zip, 55 MB)
  12. 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.
  13. 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.