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A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis
Computer ScienceScientific Reports

A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis

Q. Hu, Y. Peng, et al.

A powerful new deep-learning approach dramatically boosts speech emotion recognition, improving accuracy by up to 15% over prior methods and enabling real-time analysis for applications like live TV audience monitoring. Research conducted by the authors listed in the <Authors> tag: Qingqing Hu, Yiran Peng, and Zhong Zheng, showcases CNN and LSTM-driven models that decode emotions such as happiness, sadness, anger, fear, surprise, and neutrality.... show more
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