Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset

Rochlani, Yogesh R.; Raut, Anjali B.

doi:10.1109/icaect60202.2024.10468810

2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) 2024

DOI: 10.1109/icaect60202.2024.10468810

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Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset

Yogesh R. Rochlani,

Anjali B. Raut

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2024

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Cited by 2 publications

References 30 publications

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Audio-visual expression-based emotion recognition model for neglected people in real-time: a late-fusion approach

Hore,

Bhattacharya

2024

Multimed Tools Appl

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Audio-visual expression-based emotion recognition model for neglected people in real-time: a late-fusion approach

Hore,

Bhattacharya

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

Yan,

Yue,

et al. 2024

Electronics

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In order to obtain more fine-grained information from multiple sub-feature spaces for domain adaptation, this paper proposes a novel multi-representation joint dynamic domain adaptation network (MJDDAN) and applies it to achieve cross-database facial expression recognition. The MJDDAN uses a hybrid structure to extract multi-representation features and maps the original facial expression features into multiple sub-feature spaces, aligning the expression features of the source domain and target domain in multiple sub-feature spaces from different angles to extract features more comprehensively. Moreover, the MJDDAN proposes the Joint Dynamic Maximum Mean Difference (JD-MMD) model to reduce the difference in feature distribution between different subdomains by simultaneously minimizing the maximum mean difference and local maximum mean difference in each substructure. Three databases, including eNTERFACE, FABO, and RAVDESS, are used to design a large number of cross-database transfer learning facial expression recognition experiments. The accuracy of emotion recognition experiments with eNTERFACE, FABO, and RAVDESS as target domains reach 53.64%, 43.66%, and 35.87%, respectively. Compared to the best comparison method chosen in this article, the accuracy rates were improved by 1.79%, 0.85%, and 1.02%, respectively.

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Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset

Cited by 2 publications

References 30 publications

Audio-visual expression-based emotion recognition model for neglected people in real-time: a late-fusion approach

Audio-visual expression-based emotion recognition model for neglected people in real-time: a late-fusion approach

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

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