Combined CNN LSTM with attention for speech emotion recognition based on feature-level fusion
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/s11042-023-17829-x.pdf
Reference49 articles.
1. Chen M, Zhou P, Fortino G (2016) Emotion communication system. IEEE. Access 5:326–337. https://doi.org/10.1109/ACCESS.2016.2641480
2. Pravena D, Govind D (2017) Significance of incorporating excitation source parameters for improved emotion recognition from speech and electroglottographic signals. Int J Speech Technol 20:787–797. https://doi.org/10.1007/s10772-017-9445-x
3. Lado-Codesido M, Méndez Pérez C, Mateos R et al (2019) Improving emotion recognition in schizophrenia with “VOICES”: an on-line prosodic self-training. PLoS One 14(1):e0210816. https://doi.org/10.1371/journal.pone.0210816
4. Schelinski S, von Kriegstein K (2019) The relation between vocal pitch and vocal emotion recognition abilities in people with autism spectrum disorder and typical development. J Autism Dev Disord 49:68–82. https://doi.org/10.1007/s10803-018-3681-z
5. Paris M, Mahajan Y, Kim J (2018) Emotional speech processing deficits in bipolar disorder: The role of mismatch negativity and P3a. J Affect Disord 234:261–269. https://doi.org/10.1016/j.jad.2018.02.026
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