1 citations · 4 across the 19 of their papers we have counts for
6 papers · 1 filter
Towards Audio Token Compression in Large Audio Language Models
Saurabhchand Bhati, Samuel Thomas, Hilde Kuehne +2
Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate token sequences (e.g., 25 tokens/s), makin…
Towards Unsupervised Speech Recognition at the Syllable-Level
Liming Wang, Junrui Ni, Kai-Wei Chang +4
Training speech recognizers with unpaired speech and text -- known as unsupervised speech recognition (UASR) -- is a crucial step toward extending ASR to low-resource languages in…
Recognizing Dementia from Neuropsychological Tests with State Space Models
Liming Wang, Saurabhchand Bhati, Cody Karjadi +2
Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have tr…
USAD: Universal Speech and Audio Representation via Distillation
Heng-Jui Chang, Saurabhchand Bhati, James Glass +1
Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we pr…
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
Edson Araujo, Andrew Rouditchenko, Yuan Gong +7
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations t…
Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo +4
We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new St…