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Found 10 papers, 4 papers with code
Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration
This improvement stems from our integration of the strengths of LLMs and symbolic systems, which enables a more reliable and interpretable approach for the GPS task.
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation Models
We also introduce a multi-teacher ensemble method to distill the student, which receives audio-visual data as inputs.
Ranked #1 on
Automatic Speech Recognition (ASR)
on LRS3-TED
Audio-Visual Speech Recognition
Automatic Speech Recognition
+6
Latent Swap Joint Diffusion for 2D Long-Form Latent Generation
To address this issue, we propose Self-Loop Latent Swap, a frame-level bidirectional swap applied to the overlapping region of adjacent views.
Deep CLAS: Deep Contextual Listen, Attend and Spell
In this work, we propose deep CLAS to use contextual information better.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge.
Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
Text-to-music (TTM) generation, which converts textual descriptions into audio, opens up innovative avenues for multimedia creation.
Ranked #1 on
Music Generation
on Song Describer Dataset
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
This pioneering effort aims to set the first benchmark for the AVTSE task, offering fresh insights into enhancing the ac-curacy of back-end speech recognition systems through AVTSE in challenging and real acoustic environments.
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge
This technical report details our submission system to the CHiME-7 DASR Challenge, which focuses on speaker diarization and speech recognition under complex multi-speaker scenarios.
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit
Speech pre-training has shown great success in learning useful and general latent representations from large-scale unlabeled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation
AV2vec has a student and a teacher module, in which the student performs a masked latent feature regression task using the multimodal target features generated online by the teacher.
