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cs.CL2025
Closing the Gap Between Text and Speech Understanding in LLMs
Santiago Cuervo, Skyler Seto, Maureen de Seyssel +5
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counte…
cs.CL2025
Leveraging Audio-Visual Data to Reduce the Multilingual Gap in Self-Supervised Speech Models
María Andrea Cruz Blandón, Zakaria Aldeneh, Jie Chi +1
Self-supervised learning (SSL) has made significant advances in speech representation learning. Models like wav2vec 2.0 and HuBERT have achieved state-of-the-art results in tasks s…