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Title: Learning Cross-Modal Audiovisual Representations with Ladder Networks for Emotion Recognition
Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audio-visual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific in-formation while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem.  more » « less
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Author(s) / Creator(s):
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Journal Name:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
Page Range / eLocation ID:
1 to 5
Medium: X
Sponsoring Org:
National Science Foundation
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