activity
20242026
most citedAn Improved Empirical Fisher Approximation for Natural Gradient Descent

1 citations · 1 across the 6 of their papers we have counts for

collaborators

9 papers

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.AI2026

Talk2Agent: Benchmarking Voice Interfaces for Text Agents

Terumi Chiba, Guangzhi Sun, Zheqi Yuan +1

Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular interface for interacting with…

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…

eess.AS2025

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…

cs.AI2025

End-to-end Listen, Look, Speak and Act

Siyin Wang, Wenyi Yu, Xianzhao Chen +4

Human interaction is inherently multimodal and full-duplex: we listen while watching, speak while acting, and fluidly adapt to turn-taking and interruptions. Realizing these capabi…

eess.AS2025

MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence

Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar +31

Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…

eess.AS2024

Extract and Diffuse: Latent Integration for Improved Diffusion-based Speech and Vocal Enhancement

Yudong Yang, Zhan Liu, Wenyi Yu +3

Diffusion-based generative models have recently achieved remarkable results in speech and vocal enhancement due to their ability to model complex speech data distributions. While t…

cs.LG2024★ 1 cited

An Improved Empirical Fisher Approximation for Natural Gradient Descent

Xiaodong Wu, Wenyi Yu, Chao Zhang +1

Approximate Natural Gradient Descent (NGD) methods are an important family of optimisers for deep learning models, which use approximate Fisher information matrices to pre-conditio…