Human Vocal Sentiment Analysis
@article{Huang2019HumanVS, title={Human Vocal Sentiment Analysis}, author={Andrew Huang and Puwei Bao}, journal={ArXiv}, year={2019}, volume={abs/1905.08632} }
In this paper, we use several techniques with conventional vocal feature extraction (MFCC, STFT), along with deep-learning approaches such as CNN, and also context-level analysis, by providing the textual data, and combining different approaches for improved emotion-level classification. [] Key Method We apply hyperparameter sweeps and data augmentation to improve performance. Finally, we see if a real-time approach is feasible, and can be readily integrated into existing systems.
14 Citations
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Hierarchical classifier design for speech emotion recognition in the mixed-cultural environment
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A two-level hierarchical engine has been designed to identify emotion from the speech of different cultural backgrounds, using a discriminative, multiclass SVM classifier trained with the emotional utterances of that particular corpus.
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