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Cited by in F6Publishing
For: Liu D, Wang Z, Wang L, Chen L. Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning. Front Neurorobot 2021;15:697634. [PMID: 34305565 DOI: 10.3389/fnbot.2021.697634] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Dvoynikova A, Markitantov M, Ryumina E, Uzdiaev M, Velichko A, Ryumin D, Lyakso E, Karpov A. Analysis of infoware and software for human affective states recognition. IA 2022;21:1097-1144. [DOI: 10.15622/ia.21.6.2] [Reference Citation Analysis]
2 Prasad R, Tarai S, Bit A. Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions. Cogn Neurodyn. [DOI: 10.1007/s11571-022-09888-x] [Reference Citation Analysis]
3 Jayanthi K, Mohan S, B L. An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach. Int j inf tecnol . [DOI: 10.1007/s41870-022-00900-5] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
4 Dresvyanskiy D, Ryumina E, Kaya H, Markitantov M, Karpov A, Minker W. End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild. MTI 2022;6:11. [DOI: 10.3390/mti6020011] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
5 [DOI: 10.1109/mysurucon52639.2021.9641642] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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