Survey of Deep Representation Learning for Speech Emotion Recognition

Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak, Junaid Qadir, Bjorn Schuller

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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 effort, 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 deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This article 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.

Original languageEnglish
Pages (from-to)1634-1654
Number of pages21
JournalIEEE Transactions on Affective Computing
Issue number2
StatePublished - 1 Apr 2023
Externally publishedYes


  • Speech emotion recognition
  • domain adaptation
  • multi task learning
  • representation learning
  • unsupervised learning


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