Cross-lingual deep learning model for gender-based emotion detection
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/s11042-023-16304-x.pdf
Reference45 articles.
1. Ali SA, Zehra S, Arif A (2013) Performance evaluation of learning classifiers for speech emotions corpus using combinations of prosodic features. Int J Comput Appl 76(2)
2. Ali SA, Zehra S, Khan M, Wahab F (2013) Development and analysis of speech emotion corpus using prosodic features for cross linguistics. Int J Sci Eng Res 4(1):1–8
3. Alsharif MH, Kelechi AH, Yahya K, Chaudhry SA (2020) Machine learning algorithms for smart data analysis in internet of things environment: taxonomies and research trends. Symmetry 12(1):88
4. Badshah AM, Ahmad J, Rahim N, Baik SW (2017) Speech emotion recognition from spectrograms with deep convolutional neural network. In: 2017 international conference on platform technology and service (PlatCon). IEEE, pp 1–5
5. Bhattacharya S, Das N, Sahu S, Mondal A, Borah S (2021) Deep classification of sound: A concise review. In: Proceeding of First Doctoral Symposium on Natural Computing Research: DSNCR 2020. Springer Singapore, pp 33–43
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