Survey of Deep Representation Learning for Speech Emotion Recognition

Article

Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja, Qadir, Junaid and Schuller, Bjorn. 2021. "Survey of Deep Representation Learning for Speech Emotion Recognition." IEEE Transactions on Affective Computing. 14 (2), pp. 1634-1654. https://doi.org/10.1109/TAFFC.2021.3114365
Article Title

Survey of Deep Representation Learning for Speech Emotion Recognition

ERA Journal ID200608
Article CategoryArticle
AuthorsLatif, Siddique (Author), Rana, Rajib (Author), Khalifa, Sara (Author), Jurdak, Raja (Author), Qadir, Junaid (Author) and Schuller, Bjorn (Author)
Journal TitleIEEE Transactions on Affective Computing
Journal Citation14 (2), pp. 1634-1654
Number of Pages21
Year2021
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1949-3045
Digital Object Identifier (DOI)https://doi.org/10.1109/TAFFC.2021.3114365
Web Address (URL)https://ieeexplore.ieee.org/document/9543566
Abstract

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.

Keywordsspeech emotion recognition, multi task learning, representation learning, domain adaptation, unsupervised learning
Related Output
Is part ofDeep Representation Learning for Speech Emotion Recognition
ANZSRC Field of Research 2020461101. Adversarial machine learning
460212. Speech recognition
461102. Context learning
461105. Reinforcement learning
461106. Semi- and unsupervised learning
461104. Neural networks
461103. Deep learning
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This article is part of a UniSQ Thesis by publication. See Related Output.

Byline AffiliationsSchool of Sciences
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Queensland University of Technology
Information Technology University, Pakistan
Imperial College London, United Kingdom
Institution of OriginUniversity of Southern Queensland
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