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Arxiv
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Risk-Calibrated Affective Speech Recognition via Conformal Coverage Guarantees: A Stochastic Calibrative Framework for Emergent Uncertainty Quantification
- Traffic safety challenges arising from extreme driver emotions highlight the urgent need for reliable emotion recognition systems.
- Traditional deep learning approaches in speech emotion recognition suffer from overfitting and poorly calibrated confidence estimates.
- A framework integrating Conformal Prediction (CP) and Risk Control is proposed, using Mel-spectrogram features processed through a pre-trained convolutional neural network.
- The Risk Control framework enables task-specific adaptation through customizable loss functions, dynamically adjusting prediction set sizes while maintaining coverage guarantees.
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