Speech emotion recognition and classification using hybrid deep CNN and BiLSTM model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16849-x.pdf
Reference43 articles.
1. Chen J, Wang C, Wang K et al (2021) HEU Emotion: a large-scale database for multimodal emotion recognition in the wild. Neural Comput Appl 33:8669–8685. https://doi.org/10.1007/s00521-020-05616-w
2. Zeng Y, Mao H, Peng D (2019) Spectrogram-based multi-task audio classification. Multimed Tools Appl 78:3705–3722. https://doi.org/10.1007/s11042-017-5539-3
3. Jahangir R, Teh YW, Hanif F et al (2021) Deep learning approaches for speech emotion recognition: state of the art and research challenges. Multimed Tools Appl 80:23745–23812. https://doi.org/10.1007/s11042-020-09874-7
4. Jaiswal S, Nandi GC (2020) Robust real-time emotion detection system using CNN architecture. Neural Comput Appl 32:11253–11262. https://doi.org/10.1007/s00521-019-04564-4
5. Likitha MS, Gupta SRR, Hasitha K, Raju AU (2017) Speech-based human emotion recognition using MFCC. In: 2017 international conference on wireless communications, signal processing and networking (WiSPNET), pp 2257–2260. https://doi.org/10.1109/WiSPNET.2017.8300161
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