The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models
Open accessAbstract
The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Cited by
Alam, M. S. B., Lameesa, A., Sharmin, S., Afrin, S., Ahmed, S. F., Nikoo, M. R., & Gandomi, A. H. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2025.04.001 · Cited by 9
A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition
(2023) · doi:10.3390/math11224573 · Cited by 3
Metadata
- Authors
- Abbaschian, B. J., Sierra-Sosa, D., & Elmaghraby, A.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-02-10
- Volume
- 21
- Issue
- 4
- Pages
- 1249
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3126625480
- Cited by
- 293
- References
- 84
- FWCI
- 50.15
- Type
- journal article
- Language
- en
- Open access
- gold
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