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Found 5 papers, 0 papers with code
Active Voice Authentication
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.
Cycle-Consistent Speech Enhancement
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.
Adversarial Feature-Mapping for Speech Enhancement
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.
Speaker-Invariant Training via Adversarial Learning
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.
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
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.
