Large self-supervised pre-trained speech models require computationally
...
In this paper, we propose ACA-Net, a lightweight, global context-aware
s...
Prior studies diagnose the anisotropy problem in sentence representation...
Most of the existing neural-based models for keyword spotting (KWS) in s...
In this paper, we derive a new class of doubly robust estimators for
tre...
Learning on a massive amount of speech corpus leads to the recent succes...
Existing self-supervised pre-trained speech models have offered an effec...
Noise robustness in keyword spotting remains a challenge as many models ...
Sparsity in Deep Neural Networks (DNNs) has been widely studied to compr...
Existing algorithms aiming to learn a binary classifier from positive (P...
Batch Normalization (BN) was shown to accelerate training and improve
ge...
The Chinese pronunciation system offers two characteristics that disting...
This paper leverages heterogeneous auxiliary information to address the ...
In this work, we focus on effectively leveraging and integrating informa...