research-article

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Authors:

Jürgen Geiger,

Jouni Pohjalainen,

Amr El-Desoky Mousa,

Björn SchullerAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 5

Article No.: 49, Pages 1 - 28

https://doi.org/10.1145/3178115

Published: 24 April 2018 Publication History

Abstract

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but still remains an important challenge. Data-driven supervised approaches, especially the 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. In the meanwhile, we discuss the pros and cons of these approaches and provide their experimental results on benchmark databases. We expect that this overview can facilitate the development of the robustness of speech recognition systems in acoustic noisy environments.

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Index Terms

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 5

Research Survey and Regular Papers

September 2018

274 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3210369

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Published: 24 April 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 July 2017

Published in TIST Volume 9, Issue 5

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https://doi.org/10.3233/JIFS-238159
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