A Primer on Deep Learning Architectures and Applications in Speech Processing

In the recent past years, deep-learning-based machine learning methods have demonstrated remarkable success for a wide range of learning tasks in multiple domains. They are suitable for complex classification and regression problems in applications such as computer vision, speech recognition and oth...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2019-08, Vol.38 (8), p.3406-3432
Main Authors: Ogunfunmi, Tokunbo, Ramachandran, Ravi Prakash, Togneri, Roberto, Zhao, Yuanjun, Xia, Xianjun
Format: Article
Language:English
Subjects:
Algorithms
Circuits and Systems
Computer vision
Deep learning
Domains
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Machine learning
Pattern analysis
Regression analysis
Signal,Image and Speech Processing
Speech processing
Speech recognition
State-of-the-art reviews
Task complexity
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Summary:In the recent past years, deep-learning-based machine learning methods have demonstrated remarkable success for a wide range of learning tasks in multiple domains. They are suitable for complex classification and regression problems in applications such as computer vision, speech recognition and other pattern analysis branches. The purpose of this article is to contribute a timely review and introduction of state-of-the-art and popular discriminative DNN, CNN and RNN deep learning techniques, the basic framework and algorithms, hardware implementations, applications in speech, and the overall benefits of deep learning.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-019-01157-3