Aug 25, 2016 · Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech ...
Oct 2, 2016 · Abstract:This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for ...
Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate WER for noise robust speech recognition. The best ...
The proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech recognition and are competitive with the long short-term ...
The extension and optimisation of previous work on very deep convolutional neural networks for effective recognition of noisy speech in the Aurora 4 task ...
Sep 8, 2024 · This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for effective ...
Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech recognition. The best ...
Adaptive Very Deep Convolutional Residual Network for Noise Robust ...
ieeexplore.ieee.org › document
Apr 12, 2018 · Based on our work on VDCNNs, this paper proposes a more advanced model referred to as the very deep convolutional residual network (VDCRN). This ...
People also ask
Which neural network is best for speech recognition?
Is CNN good for speech recognition?
What is noise robust speech recognition?
What is the difference between CNN and deep CNN?
Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech recognition. The best ...
Abstract. In this paper, we present a framework of a factored deep con- volutional neural network (CNN) learning for noise robust au- tomatic speech ...