TY - JOUR
T1 - A Primer on Deep Learning Architectures and Applications in Speech Processing
AU - Ogunfunmi, Tokunbo
AU - Ramachandran, Ravi Prakash
AU - Togneri, Roberto
AU - Zhao, Yuanjun
AU - Xia, Xianjun
N1 - Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067281924&partnerID=8YFLogxK
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U2 - 10.1007/s00034-019-01157-3
DO - 10.1007/s00034-019-01157-3
M3 - Article
AN - SCOPUS:85067281924
SN - 0278-081X
VL - 38
SP - 3406
EP - 3432
JO - Circuits, Systems, and Signal Processing
JF - Circuits, Systems, and Signal Processing
IS - 8
ER -