Time-Frequency Transformer: A Novel Time Frequency Joint Learning Method for Speech Emotion Recognition
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8138-0_33
Reference29 articles.
1. Schuller, B., Batliner, A.: Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing. John Wiley & Sons (2013)
2. Schuller, B.W.: Speech emotion recognition: two decades in a nutshell, benchmarks, and ongoing trends. Commun. ACM 61(5), 90–99 (2018)
3. Akçay, M.B., Oğuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56–76 (2020)
4. Lu, C., Zong, Y., Zheng, W., Li, Y., Tang, C., Schuller, B.W.: Domain invariant feature learning for speaker-independent speech emotion recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 30, 2217–2230 (2022)
5. Stuhlsatz, A., Meyer, C., Eyben, F., Zielke, T., Meier, G., Schuller, B.: Deep neural networks for acoustic emotion recognition: raising the benchmarks. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5688–5691. IEEE (2011)
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