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20232026
most citedSALMONN: Towards Generic Hearing Abilities for Large Language Models

20 citations · 28 across the 15 of their papers we have counts for

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5 papers · 1 filter

eess.AS2026

Full-Duplex Interaction with Speech, Expressions and Gestures

Siyin Wang, Wenyi Yu, Terumi Chiba +5

Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-to-end full-duplex dialogue mo…

cs.SD2026

TVPara: Paralinguistic Captioning of Expressive TV-Series Speech in Complex Acoustic Environments

Ziyang Zhang, Wenyi Yu, Wen Wu +2

Large audio-language models (LALMs) struggle to parse paralinguistics (e.g., emotion, pace, pitch) in speech that combines intense emotion with complex acoustic environments. Stren…

cs.CL2026

SALMONN-duo: Adaptive Dual-System Coordination for Full-Duplex Voice Agents

Wenyi Yu, Siyin Wang, Terumi Chiba +5

Full-duplex speech large language models (LLMs) enable low-latency, natural voice interaction. However, real-world agents must also use tools and perform deliberative reasoning-ope…

cs.LG2026

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

Xiaodong Wu, Wenyi Yu, Chao Zhang +1

Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletely understood. We investigate…

eess.AS2026

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…