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A Primer on Deep Learning Architectures and Applications in Speech Processing

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Abstract

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.

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  1. https://www.aldec.com/en/company/blog/167--fpgas-vs-gpus-for-machine-learning-applications-which-one-is-better.

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  1. Department of Electrical Engineering, Santa Clara University, Santa Clara, USA

    Tokunbo Ogunfunmi

  2. Department of Electrical and Computer Engineering, Rowan University, Glassboro, USA

    Ravi Prakash Ramachandran

  3. Department of EEC Engineering, The University of Western Australia, Perth, Australia

    Roberto Togneri, Yuanjun Zhao & Xianjun Xia

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Ogunfunmi, T., Ramachandran, R.P., Togneri, R. et al. A Primer on Deep Learning Architectures and Applications in Speech Processing. Circuits Syst Signal Process 38, 3406–3432 (2019). https://doi.org/10.1007/s00034-019-01157-3

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