Cited by 554Emotion and Mood RecognitionSpeech Recognition and SynthesisSpeech and Audio Processing

Evaluating deep learning architectures for Speech Emotion Recognition

Fayek, Haytham M., Lech, Margaret, Cavedon, Lawrence·2017·Neural Networks

Abstract

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances.

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Crossref · 10.1016/j.neunet.2017.02.013

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Authors
Fayek, H. M., Lech, M., & Cavedon, L.
Affiliations
RMIT University (AU)
Corresponding author
Fayek, Haytham M.
Venue
Neural Networks
Publisher
Elsevier BV
Published
2017-08
Volume
92
Pages
60-68
ISSN
0893-6080 (ISSN-L)
OpenAlex
W2602034649
Cited by
554
References
56
Citations by year
2026: 31 · 2025: 42 · 2024: 44 · 2023: 78 · 2022: 72 · 2021: 92 · 2020: 92 · 2019: 67 · 2018: 28 · 2017: 8
FWCI
69.44
Type
journal article
Language
en
Open access
closed
Locations
  • Neural Networks (publishedVersion)
  • PubMed (publishedVersion)
  • RMIT Research Repository (RMIT University Library) (submittedVersion)
  • Figshare (submittedVersion)
Funders
  • RMIT University
  • NVIDIA Corporation
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords
speech emotion recognition, deep learning architectures, recurrent neural networks, feedforward neural networks, paralinguistic features, IEMOCAP, speaker-independent emotion recognition, end-to-end learning
MeSH terms
Machine Learning, Emotions, Neural Networks, Computer, Speech Recognition Software

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