no code implementations • 22 Nov 2022 • Vinay Kothapally, John H. L. Hansen
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
no code implementations • 22 Nov 2022 • Vinay Kothapally, J. H. L. Hansen
With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements.
no code implementations • 22 Nov 2022 • Vinay Kothapally, Yong Xu, Meng Yu, Shi-Xiong Zhang, Dong Yu
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.
no code implementations • 9 Nov 2021 • Vinay Kothapally, Yong Xu, Meng Yu, Shi-Xiong Zhang, Dong Yu
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.
no code implementations • 17 Jul 2020 • Vinay Kothapally, Wei Xia, Shahram Ghorbani, John H. L. Hansen, Wei Xue, Jing Huang
The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications.
no code implementations • 15 Oct 2019 • Salar Jafarlou, Soheil Khorram, Vinay Kothapally, John H. L. Hansen
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