Using regional saliency for speech emotion recognition
Abstract
In this paper, we show that convolutional neural networks can be directly applied to temporal low-level acoustic features to identify emotionally salient regions without the need for defining or applying utterance-level statistics. We show how a convolutional neural network can be applied to minimally hand-engineered features to obtain competitive results on the IEMOCAP and MSP-IMPROV datasets. In addition, we demonstrate that, despite their common use across most categories of acoustic features, utterance-level statistics may obfuscate emotional information. Our results suggest that convolutional neural networks with Mel Filterbanks (MFBs) can be used as a replacement for classifiers that rely on features obtained from applying utterance-level statistics.
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Data augmentation to artificially increase the dataset size is used less often than for FER; notable examples include addition of Gaussian noise, different sampling frequencies, and modified playback speed.
“Data augmentation to artificially increase the dataset size is used less often than for FER; notable examples include the addition of Gaussian noise [129], different sampling frequencies [130], and modified playback speed [142].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [142]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
The INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938 · see citing works
Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2360798 · Cited by 568 · see citing works
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception
Busso, C., Parthasarathy, S., Burmania, A., AbdelWahab, M., Sadoughi, N., & Provost, E. M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2515617 · Cited by 391 · see citing works
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Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Attention Based Fully Convolutional Network for Speech Emotion Recognition
Zhang, Y., Du, J., Wang, Z., Zhang, J., & Tu, Y. (2018) · 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659587 · Cited by 161 · see citing works
Zhou, H., Du, J., Zhang, Y., Wang, Q., Liu, Q.-F., & Lee, C.-H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3096037 · Cited by 85 · see citing works
- Authors
- Aldeneh, Z., & Provost, E. M.
- Affiliations
- University of Michigan (US)
- Venue
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 2741-2745
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2648194195
- Cited by
- 126
- References
- 4
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 8 · 2022: 14 · 2021: 35 · 2020: 20 · 2019: 23 · 2018: 13 · 2017: 1
- FWCI
- 17.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- 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 › Music and Audio Processing
- Keywords
- speech emotion recognition, convolutional neural networks, IEMOCAP
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