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Speech Recognition Using Deep Neural Networks: A Systematic Review

TLDR
A thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications is provided.
Abstract
Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.

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Posted Content

A Survey of Deep Active Learning

TL;DR: A formal classification method for the existing work in deep active learning is provided, along with a comprehensive and systematic overview, to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL.
Journal ArticleDOI

Speech Emotion Recognition Using Deep Learning Techniques: A Review

TL;DR: An overview of Deep Learning techniques is presented and some recent literature where these methods are utilized for speech-based emotion recognition is discussed, including databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.
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References
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Book

Reinforcement Learning: An Introduction

TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI

Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Book

Learning Deep Architectures for AI

TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Journal ArticleDOI

Learning long-term dependencies with gradient descent is difficult

TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.
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