Open AccessPosted Content
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Zixing Zhang,Jürgen T. Geiger,Jouni Pohjalainen,Amr El-Desoky Mousa,Wenyu Jin,Björn Schuller +5 more
TLDR
Recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems are reviewed.Abstract:
Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.read more
Citations
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Journal ArticleDOI
Deep learning on image denoising: An overview.
TL;DR: A comparative study of deep techniques in image denoising by classifying the deep convolutional neural networks for additive white noisy images, the deep CNNs for real noisy images; the deepCNNs for blind Denoising and the deep network for hybrid noisy images.
Journal ArticleDOI
Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
TL;DR: This article constructs an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning and develops a two-sided matching scheme and a deep reinforcementLearning approach to schedule offloading requests and allocate network resources.
Posted ContentDOI
Recent Advances in Deep Learning: An Overview
Matiur Rahman Minar,Jibon Naher +1 more
TL;DR: This paper is going to briefly discuss about recent advances in Deep Learning for past few years.
Posted Content
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends.
TL;DR: This paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning by bringing together the scattered research across three distinct research areas including Automatic Speech Recognition, Speaker Recognition (SR), and Speaker Emotion recognition (SER).
Journal ArticleDOI
Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges
Sid-Ahmed Boukabara,Vladimir M. Krasnopolsky,Jebb Stewart,Eric Maddy,Narges Shahroudi,Ross N. Hoffman +5 more
TL;DR: It is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.
References
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