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arXiv · 2204.09227

Cross-stitched Multi-modal Encoders

Karan Singla·Daniel Pressel·Ryan Price·Bhargav Srinivas Chinnari·Yeon-Jun Kim·Srinivas Bangalore
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Abstract

In this paper, we propose a novel architecture for multi-modal speech and text input. We combine pretrained speech and text encoders using multi-headed cross-modal attention and jointly fine-tune on the target problem. The resultant architecture can be used for continuous token-level classification or utterance-level prediction acting on simultaneous text and speech. The resultant encoder efficiently captures both acoustic-prosodic and lexical information. We compare the benefits of multi-headed attention-based fusion for multi-modal utterance-level classification against a simple concatenation of pre-pooled, modality-specific representations. Our model architecture is compact, resource efficient, and can be trained on a single consumer GPU card.

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Karan Singla, Daniel Pressel, Ryan Price, Bhargav Srinivas Chinnari, Yeon-Jun Kim, Srinivas Bangalore. 2022-04-20. Cross-stitched Multi-modal Encoders. https://arxiv.org/abs/2204.09227

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