Linear Frequency Residual Cepstral Coefficients for Speech Emotion Recognition-Reference-Cited by-同舟云学术

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Linear Frequency Residual Cepstral Coefficients for Speech Emotion Recognition

Published:2023 Issue: Volume: Page:116-129
ISSN:0302-9743
Container-title:Speech and Computer
language:
Short-container-title:

Author:

Hora Baveet Singh,Uthiraa S.,Patil Hemant A.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-48309-7_10

Reference17 articles.

1. Akçay, M.B., Oğuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56–76 (2020)

2. Anagnostopoulos, C.N., Iliou, T., Giannoukos, I.: Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011. Artif. Intell. Rev. 43, 155–177 (2015)

3. Bhangale, K., Kothandaraman, M.: Speech emotion recognition based on multiple acoustic features and deep convolutional neural network. Electronics 12(4), 839 (2023)

4. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W.F., Weiss, B., et al.: A database of german emotional speech. In: Interspeech, Lisbon, Portugal, vol. 5, pp. 1517–1520 (2005)

5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778 (2016)

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