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Found 6 papers, 0 papers with code
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation
Several speech processing systems have demonstrated considerable performance improvements when deep complex neural networks (DCNN) are coupled with self-attention (SA) networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking
With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements.
Deep Neural Mel-Subband Beamformer for In-car Speech Separation
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use.
Joint Neural AEC and Beamforming with Double-Talk Detection
We train the proposed model in an end-to-end approach to eliminate background noise and echoes from far-end audio devices, which include nonlinear distortions.
SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping
The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications.
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition
In the present study, we address this issue by investigating variants of large receptive field CNNs (LRF-CNNs) which include deeply recursive networks, dilated convolutional neural networks, and stacked hourglass networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
