8 papers
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3
Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…
MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition
Umberto Cappellazzo, Minsu Kim, Pingchuan Ma +4
Large language models (LLMs) have recently shown strong potential in audio-visual speech recognition (AVSR), but their high computational demands and sensitivity to token granulari…
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs
Umberto Cappellazzo, Minsu Kim, Stavros Petridis
Audio-Visual Speech Recognition (AVSR) leverages audio and visual modalities to improve robustness in noisy environments. Recent advances in Large Language Models (LLMs) show stron…
Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations
Jeong Hun Yeo, Minsu Kim, Chae Won Kim +2
We explore a novel zero-shot Audio-Visual Speech Recognition (AVSR) framework, dubbed Zero-AVSR, which enables speech recognition in target languages without requiring any audio-vi…
Revival with Voice: Multi-modal Controllable Text-to-Speech Synthesis
Minsu Kim, Pingchuan Ma, Honglie Chen +2
This paper explores multi-modal controllable Text-to-Speech Synthesis (TTS) where the voice can be generated from face image, and the characteristics of output speech (e.g., pace,…
Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach
Umberto Cappellazzo, Minsu Kim, Stavros Petridis +2
Audio-Visual Speech Recognition (AVSR) enhances robustness in noisy environments by integrating visual cues. While recent advances integrate Large Language Models (LLMs) into AVSR,…