The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching
Abstract
Speech emotion recognition is challenging because of the affective gap between the subjective emotions and low-level features. Integrating multilevel feature learning and model training, deep convolutional neural networks (DCNN) has exhibited remarkable success in bridging the semantic gap in visual tasks like image classification, object detection. This paper explores how to utilize a DCNN to bridge the affective gap in speech signals. To this end, we first extract three channels of log Mel-spectrograms (static, delta, and delta delta) similar to the red, green, blue (RGB) image representation as the DCNN input. Then, the AlexNet DCNN model pretrained on the large ImageNet dataset is employed to learn high-level feature representations on each segment divided from an utterance. The learned segment-level features are aggregated by a discriminant temporal pyramid matching (DTPM) strategy. DTPM combines temporal pyramid matching and optimal Lp-norm pooling to form a global utterance-level feature representation, followed by the linear support vector machines for emotion classification. Experimental results on four public datasets, that is, EMO-DB, RML, eNTERFACE05, and BAUM-1s, show the promising performance of our DCNN model and the DTPM strategy. Another interesting finding is that the DCNN model pretrained for image applications performs reasonably good in affective speech feature extraction. Further fine tuning on the target emotional speech datasets substantially promotes recognition performance.
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Cited by
Speech Emotion Recognition Using Deep Learning Techniques: A Review
Khalil, R. A., Jones, E., Babar, M. I., Jan, T., Zafar, M. H., & Alhussain, T. (2019) · IEEE Access · doi:10.1109/access.2019.2936124 · Cited by 702
A systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 615
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition
Chen, M., He, X., Yang, J., & Zhang, H. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2860246 · Cited by 503
Khare, S. K., Blanes-Vidal, V., Nadimi, E. S., & Acharya, U. R. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102019 · Cited by 469
Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM
Mustaqeem, Sajjad, M., & Kwon, S. (2020) · IEEE Access · doi:10.1109/access.2020.2990405 · Cited by 402
Metadata
- Authors
- Zhang, S., Zhang, S., Huang, T., & Gao, W.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2018-06
- Volume
- 20
- Issue
- 6
- Pages
- 1576-1590
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2766272105
- Cited by
- 441
- References
- 69
- FWCI
- 43.14
- Type
- journal article
- Language
- en
- Open access
- closed
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