Application of probabilistic neural network for speech emotion recognition

Author:

Deshmukh ShrikalaORCID,Gupta Preeti

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference48 articles.

1. Agata, K., Landowska, A., Szwoch, M., Szwoch, W., & Wrobel, M. R. (2014). Emotion recognition and its applications. In Human-computer systems interaction: Backgrounds and applications 3 (pp. 51–62). Springer.

2. Ann, V. A., Cherian, J. P., & Kizhakkethottam, J. J. (2015). Overview on emotion recognition system. In 2015 international conference on soft-computing and networks security (ICSNS), IEEE (pp. 1–5).

3. Basharirad, B., & Moradhaseli, M. (2017). Speech emotion recognition methods: A literature review. AIP Conference Proceedings, 1891(1), 020105.

4. Bhangale, K. B., & Mohanaprasad, K. (2021). A review on speech processing using machine learning paradigm. International Journal of Speech Technology, 24, 367–388.

5. Cen, L., Ser, W., & Yu, Z. L. (2008). Speech emotion recognition using canonical correlation analysis and probabilistic neural network. In 2008 seventh international conference on machine learning and applications (pp. 859–862).

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1. A Combined CNN Architecture for Speech Emotion Recognition;Sensors;2024-09-06

2. ADMRF: Elucidation of deep feature extraction and adaptive deep Markov random fields with improved heuristic algorithm for speech emotion recognition;International Journal of Speech Technology;2024-07-12

3. BSER: A Learning Framework for Bangla Speech Emotion Recognition;2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);2024-05-02

4. Review of Databases used for Text Based Emotion Detection;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25

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