158 citations · 646 across the 34 of their papers we have counts for
44 papers
Omni-AVSR: Towards Unified Multimodal Speech Recognition with Large Language Models
Umberto Cappellazzo, Xubo Liu, Pingchuan Ma +2
Large language models (LLMs) have recently achieved impressive results in speech recognition across multiple modalities, including Auditory Speech Recognition (ASR), Visual Speech…
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…
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,…
Large Language Models are Strong Audio-Visual Speech Recognition Learners
Umberto Cappellazzo, Minsu Kim, Honglie Chen +5
Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and…
Dynamic Data Pruning for Automatic Speech Recognition
Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez +7
The recent success of Automatic Speech Recognition (ASR) is largely attributed to the ever-growing amount of training data. However, this trend has made model training prohibitivel…
MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization
Adriana Fernandez-Lopez, Honglie Chen, Pingchuan Ma +5
Pre-trained models have been a foundational approach in speech recognition, albeit with associated additional costs. In this study, we propose a regularization technique that facil…