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This work aims to promote broader adoption of OT in speech processing by reviewing OT foundations through intuitive physical interpretations and highlighting connections to modern generative models, and demonstrating OT applications in cross-domain and cross-modal speech tasks.
An optimal transport (OT)-based semantic alignment framework for LLM-AVSR is proposed, which explicitly bridges the modality gap by aligning the acoustic and visual representations with reference to the linguistic embedding space of the LLM before multimodal fusion.
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