The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Speech Emotion Recognition Using Fourier Parameters
Abstract
Recently, studies have been performed on harmony features for speech emotion recognition. It is found in our study that the first- and second-order differences of harmony features also play an important role in speech emotion recognition. Therefore, we propose a new Fourier parameter model using the perceptual content of voice quality and the first- and second-order differences for speaker-independent speech emotion recognition. Experimental results show that the proposed Fourier parameter (FP) features are effective in identifying various emotional states in speech signals. They improve the recognition rates over the methods using Mel frequency cepstral coefficient (MFCC) features by 16.2, 6.8 and 16.6 points on the German database (EMODB), Chinese language database (CASIA) and Chinese elderly emotion database (EESDB). In particular, when combining FP with MFCC, the recognition rates can be further improved on the aforementioned databases by 17.5, 10 and 10.5 points, respectively.
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Survey on speech emotion recognition: Features, classification schemes, and databases
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“Speech emotion recognition is particularly useful in man-machine interaction [1], [6].”
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Cited by
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806
Akçay, M. B., & Oğuz, K. (2020) · Speech Communication · doi:10.1016/j.specom.2019.12.001 · Cited by 721
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
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
Metadata
- Authors
- Wang, K., An, N., Li, B. N., Zhang, Y., & Li, L.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2015-01-01
- Volume
- 6
- Issue
- 1
- Pages
- 69-75
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2023937851
- Cited by
- 408
- References
- 53
- FWCI
- 40.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, K., An, N., Li, B. N., Zhang, Y., & Li, L. (2015). Speech Emotion Recognition Using Fourier Parameters. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2015.2392101
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