no code implementations • 25 Apr 2020 • Zhong Meng, M Umair Bin Altaf, Biing-Hwang, Juang
In our off-line evaluation on this dataset, the system achieves an average windowed-based equal error rates of 3-4% depending on the model configuration, which is remarkable considering that only 1 second of voice data is used to make every single authentication decision.
no code implementations • 6 Sep 2018 • Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang, Juang
In this paper, we propose a cycle-consistent speech enhancement (CSE) in which an additional inverse mapping network is introduced to reconstruct the noisy features from the enhanced ones.
no code implementations • 6 Sep 2018 • Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang, Juang
To achieve better performance on ASR task, senone-aware (SA) AFM is further proposed in which an acoustic model network is jointly trained with the feature-mapping and discriminator networks to optimize the senone classification loss in addition to the AFM losses.
no code implementations • 2 Apr 2018 • Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang, Juang
We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system.
no code implementations • 2 Apr 2018 • Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang, Juang
In this method, a student acoustic model and a condition classifier are jointly optimized to minimize the Kullback-Leibler divergence between the output distributions of the teacher and student models, and simultaneously, to min-maximize the condition classification loss.