Sparse MERIT uses frame-wise sparse mixture-of-experts with task-specific gating on self-supervised speech features to jointly optimize enhancement and emotion recognition, reporting gains over baselines on MSP-Podcast at low SNR.
Towards robust speech emotion recognition using deep resid- ual networks for speech enhancement,
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Speech from self-control tasks in remote learning shows perceptible emotional variations along valence, arousal, and dominance that can be automatically predicted.
citing papers explorer
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Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition
Sparse MERIT uses frame-wise sparse mixture-of-experts with task-specific gating on self-supervised speech features to jointly optimize enhancement and emotion recognition, reporting gains over baselines on MSP-Podcast at low SNR.
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Toward using Speech to Sense Student Emotion in Remote Learning Environments
Speech from self-control tasks in remote learning shows perceptible emotional variations along valence, arousal, and dominance that can be automatically predicted.