A transfer-learned ResNet is reported to achieve 98.94% clean and 91.21% noisy digit recognition accuracy on Aurora-2, beating CNN and LSTM baselines, but the comparison is under-specified.
Enhancing cochlear implant signal coding with scaled dot-product attention,
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Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments
A transfer-learned ResNet is reported to achieve 98.94% clean and 91.21% noisy digit recognition accuracy on Aurora-2, beating CNN and LSTM baselines, but the comparison is under-specified.