Selective classification for speech emotion recognition (Reject options)

• First study in speech emotion recognition aimed at developing a reliable system via uncertainty modeling through reject options

• Reject Options: Abstaining from classification when in doubt to improve the reliability of a emotion recognition model

• Utilized Bayesian empirical risk minimization framework, probabilistic backpropogation technique to model uncertainty in predictions and illustrated using a reject options framework

• Achievements: Classification - Up to 26% gains in F1-Score with 75% test coverage

• Rejected samples showed lower inter-evaluator agreement compared to accepted samples, validating the success of this idea in speech emotion recognition

Conference poster

Paper

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