3 accepted papers
Vision-language models heavily rely on visual representations, yet ensuring its efficiency remains a critical challenge. Most existing approaches focus on reducing visual tokens either at the visual encoder phase or during the LLM decoder stage. Inspired by human visual cognition, where an initial g…
Current speaker verification techniques heavily rely on the utilization of neural networks to extract accurate and discriminative speaker representations. In this paper, we present Branchformer based TDNN (B-TDNN), a novel architecture for extracting speaker embeddings by capturing both global and l…
Considering the bimodal nature of human speech perception, lips, and teeth movement has a pivotal role in automatic speech recognition. Benefiting from the correlated and noise-invariant visual information, audio-visual recognition systems enhance robustness in multiple scenarios. In previous work,…