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Data Augmentation Technology for Dysarthria Assistive Systems
The word error rate can be reduced from 64. 42% to 4. 39% in the case of patients with Free-talk.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer.
