3 citations · 6 across the 14 of their papers we have counts for
8 papers · 1 filter
SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations
Xiaoyu Yang, Xuenan Xu, Wenyi Yu +10
Recent audio large language models (ALLMs) are typically built upon audio encoders trained with large amounts of supervised data. Since self-supervised learning (SSL) audio encoder…
FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations
Haolong Zheng, Siyin Wang, Xulin Fan +2
Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition. However, most auditory large langu…
Augmenting Open-Vocabulary Dysarthric Speech Assessment with Human Perceptual Supervision
Kaimeng Jia, Minzhu Tu, Zengrui Jin +2
Dysarthria is a speech disorder characterized by impaired intelligibility and reduced communicative effectiveness. Automatic dysarthria assessment provides a scalable, cost-effecti…
Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Siyin Wang, Zengrui Jin, Changli Tang +26
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
Siyin Wang, Wenyi Yu, Xianzhao Chen +7
This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical…
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless…