Optimized Analysis of Emotion Recognition Through Speech Signals-Reference-Cited by-同舟云学术

Optimized Analysis of Emotion Recognition Through Speech Signals

Published:2024 Issue: Volume: Page:39-51
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Kakulapati V.^ORCID,Sahith ,Naresh ,Swethan

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8451-0_4

Reference15 articles.

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2. Kakulapati V et al (2022) Multimodal analysis of cognitive and social psychology effects of COVID 19 victims. Book series of Springer entitled. In: Decision sciences for COVID-19. International series in operations research & management science, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-030-87019-5_15

3. Sun Y, Wen G (2017) Ensemble softmax regression model for speech emotion recognition. Multim Tools Appl 76(6):8305–8328

4. Ghai M, Lal S, Duggal S, Manik S (2017) Emotion recognition on speech signals using machine learning. In: Proceedings of international conference on big data analytics and computational intelligence (ICBDAC), pp 34–39

5. Ge R, Wang CH, Xu X et al (2017) Action recognition with hierarchical convolutional neural networks features and bi-directional long short-term memory model. Control Theory Appl 34(6):790–796

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1. Enhancing Speech Emotion Recognition Through Advanced Feature Extraction and Deep Learning: A Study Using the RAVDESS Dataset;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28