An overview of noise-robust automatic speech recognition

J Li, L Deng, Y Gong… - IEEE/ACM Transactions …, 2014 - ieeexplore.ieee.org
New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …

Very deep convolutional neural networks for noise robust speech recognition

Y Qian, M Bi, T Tan, K Yu - IEEE/ACM Transactions on Audio …, 2016 - ieeexplore.ieee.org
Although great progress has been made in automatic speech recognition, significant
performance degradation still exists in noisy environments. Recently, very deep …

An investigation of deep neural networks for noise robust speech recognition

ML Seltzer, D Yu, Y Wang - 2013 IEEE international conference …, 2013 - ieeexplore.ieee.org
Recently, a new acoustic model based on deep neural networks (DNN) has been
introduced. While the DNN has generated significant improvements over GMM-based …

Feature learning in deep neural networks-studies on speech recognition tasks

D Yu, ML Seltzer, J Li, JT Huang, F Seide - arXiv preprint arXiv:1301.3605, 2013 - arxiv.org
Recent studies have shown that deep neural networks (DNNs) perform significantly better
than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech …

Investigation of speech separation as a front-end for noise robust speech recognition

A Narayanan, DL Wang - IEEE/ACM Transactions on Audio …, 2014 - ieeexplore.ieee.org
Recently, supervised classification has been shown to work well for the task of speech
separation. We perform an in-depth evaluation of such techniques as a front-end for noise …

Data augmentation using generative adversarial networks for robust speech recognition

Y Qian, H Hu, T Tan - Speech Communication, 2019 - Elsevier
For noise robust speech recognition, data mismatch between training and testing is a
significant challenge. Data augmentation is an effective way to enlarge the size and diversity …

Adaptive very deep convolutional residual network for noise robust speech recognition

T Tan, Y Qian, H Hu, Y Zhou, W Ding… - IEEE/ACM Transactions …, 2018 - ieeexplore.ieee.org
Although great progress has been made in automatic speech recognition, significant
performance degradation still exists in noisy environments. Our previous work has …

Joint noise adaptive training for robust automatic speech recognition

A Narayanan, DL Wang - 2014 IEEE International Conference …, 2014 - ieeexplore.ieee.org
We explore time-frequency masking to improve noise robust automatic speech recognition.
Apart from its use as a frontend, we use it for providing smooth estimates of speech and …

Very deep convolutional neural networks for robust speech recognition

Y Qian, PC Woodland - 2016 IEEE spoken language …, 2016 - ieeexplore.ieee.org
This paper describes the extension and optimisation of our previous work on very deep
convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora …

Generative adversarial networks based data augmentation for noise robust speech recognition

H Hu, T Tan, Y Qian - 2018 IEEE international conference on …, 2018 - ieeexplore.ieee.org
Data augmentation is an effective method to increase the size of training data and reduce
the mismatch between training and testing for noise robust speech recognition. Different …