158 citations · 672 across the 57 of their papers we have counts for
73 papers
Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners
Umberto Cappellazzo, Xubo Liu, Stavros Petridis +1
Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes…
VIB-AVSR: Variational Information Bottleneck for Noise-Robust LLM-Based Audio-Visual Speech Recognition
Piyush Arora, Navlika Singh, Umberto Cappellazzo +2
Audio-Visual Speech Recognition takes two input modalities, acoustic and visual streams, where visual information from lip movements aids recognition when audio is noisy. Recently,…
Assessing True Generalisability of Audio-Visual Speech Recognisers
Zhaofeng Lin, Stavros Petridis, Maja Pantic +1
Current Audio-Visual Speech Recognition (AVSR) models achieve near-perfect performance on the standard LRS3 benchmark, raising concerns of adaptive overfitting. To systematically a…
Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
Umberto Cappellazzo, Stavros Petridis, Maja Pantic
Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains uncle…
Pay Attention to CTC: Fast and Robust Pseudo-Labelling for Unified Speech Recognition
Alexandros Haliassos, Rodrigo Mira, Stavros Petridis
Unified Speech Recognition (USR) has emerged as a semi-supervised framework for training a single model for audio, visual, and audiovisual speech recognition, achieving state-of-th…
FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs
Andreas Zinonos, Michał Stypułkowski, Antoni Bigata +3
We present FlashLips, a two-stage, mask-free lip-sync system that decouples lips control from rendering and achieves real-time performance, with our U-Net variant running at over 1…
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…
Mitigating Attention Sinks and Massive Activations in Audio-Visual Speech Recognition with LLMs
Anand, Umberto Cappellazzo, Stavros Petridis +1
Large language models (LLMs) have recently advanced auditory speech recognition (ASR), visual speech recognition (VSR), and audio-visual speech recognition (AVSR). However, underst…
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,…
FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion
Kazuaki Mishima, Antoni Bigata Casademunt, Stavros Petridis +2
Human facial images encode a rich spectrum of information, encompassing both stable identity-related traits and mutable attributes such as pose, expression, and emotion. While rece…
KeySync: A Robust Approach for Leakage-free Lip Synchronization in High Resolution
Antoni Bigata, Rodrigo Mira, Stella Bounareli +4
Lip synchronization, known as the task of aligning lip movements in an existing video with new input audio, is typically framed as a simpler variant of audio-driven facial animatio…
KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation
Antoni Bigata, Michał Stypułkowski, Rodrigo Mira +7
Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. E…
Contextual Speech Extraction: Leveraging Textual History as an Implicit Cue for Target Speech Extraction
Minsu Kim, Rodrigo Mira, Honglie Chen +2
In this paper, we investigate a novel approach for Target Speech Extraction (TSE), which relies solely on textual context to extract the target speech. We refer to this task as Con…
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…
Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs
Alexandros Haliassos, Rodrigo Mira, Honglie Chen +3
Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised stud…
Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models
Adriana Fernandez-Lopez, Shiwei Liu, Lu Yin +2
This paper investigates the under-explored area of low-rank weight training for large-scale Conformer-based speech recognition models from scratch. Our study demonstrates the viabi…
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…
RT-LA-VocE: Real-Time Low-SNR Audio-Visual Speech Enhancement
Honglie Chen, Rodrigo Mira, Stavros Petridis +1
In this paper, we aim to generate clean speech frame by frame from a live video stream and a noisy audio stream without relying on future inputs. To this end, we propose RT-LA-VocE…
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…
EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars
Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas +3
Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but high…