Acoustic scattering signals fed into fine-tuned self-supervised deep learning models classify hair type and moisture at nearly 90% accuracy as a non-invasive alternative to visual methods.
A fine-tuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding,
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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.
Emo-Boost augments low-level deepfake detectors with intra- and inter-modal emotion consistency checks to raise cross-manipulation generalization AUC by 2.1% on FakeAVCeleb.
citing papers explorer
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Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
Acoustic scattering signals fed into fine-tuned self-supervised deep learning models classify hair type and moisture at nearly 90% accuracy as a non-invasive alternative to visual methods.
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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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EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection
Emo-Boost augments low-level deepfake detectors with intra- and inter-modal emotion consistency checks to raise cross-manipulation generalization AUC by 2.1% on FakeAVCeleb.