• Papers

  1. Mitigating Black-Box Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition (with Alexa AI)

  2. A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer (with Hu Hu, Marco, and Prof. Lee)

  3. An Iterative Fine-Tuning Approach To Neural Network Pruning for Designing Compact Audio-Visual Wake Word Spotting Systems (with Hengshun and Prof. Jun Du)

  4. When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing (with IBM Research AI)

  • ICASSP Tutorial

The proposal acceptance rate is 50% (16/32; competitively) this year.

Thank the speech and signals processing community again to recognize our works again.

I will give two (QML and Adversarial) tutorials at ICASSP 22

  • Tutorial 1: Adversarial Robustness and Reprogramming for Speech and Language Processing: Challenges and New Opportunities

with Dr. Pin-Yu Chen (IBM Research AI)

  • Tutorial 2: Quantum Neural Networks for Speech and Language Processing

with Dr. Yen-Chi Chen (CSI Brookhaven National Lab) and Dr. Jun Qi