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.
Capturing long-term temporal dependencies with convolutional JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 12 networks for continuous emotion recognition,
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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.