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Found 4 papers, 1 papers with code
Adapting OpenAI's Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets
The Mixed Error Rate results show that the amount of adaptation data may be as low as $1\sim10$ hours to achieve saturation in performance gain (SEAME) while the ASRU task continued to show performance with more adaptation data ($>$100 hours).
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation
To alleviate this problem, we propose a novel network to unify speech enhancement and separation with gradient modulation to improve noise-robustness.
Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection
We propose an uncertainty-aware model adaptation method, which is based on two motivations: 1) the estimation and exploitation of model uncertainty in a new domain is critical for reliable domain adaptation; and 2) the joint alignment of distributions for inputs (feature alignment) and outputs (self-training) is needed.
