most citedThe ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era

1 citations · 1 across the 5 of their papers we have counts for

collaborators

9 papers

eess.AS2026

Listening with Time: Precise Temporal Awareness for Long-Form Audio Understanding

Mingchen Shao, Hang Su, Wenjie Tian +6

While Large Audio Language Models (LALMs) achieve strong performance on short audio, they degrade on long-form inputs. This degradation is more severe in temporal awareness tasks,…

cs.SD20261 cited

The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era

Zhixian Zhao, Shuiyuan Wang, Guojian Li +12

Driven by the rapid advancement of Large Language Models (LLMs), particularly Audio-LLMs and Omni-models, spoken dialogue systems have evolved significantly, progressively narrowin…

eess.AS2026

The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge

Guobin Ma, Yuxuan Xia, Jixun Yao +5

This paper summarizes the ICASSP 2026 Automatic Song Aesthetics Evaluation (ASAE) Challenge, which focuses on predicting the subjective aesthetic scores of AI-generated songs. The…

eess.AS2025

DialoSpeech: Dual-Speaker Dialogue Generation with LLM and Flow Matching

Hanke Xie, Dake Guo, Chengyou Wang +8

Recent advances in text-to-speech (TTS) synthesis, particularly those leveraging large language models (LLMs), have significantly improved expressiveness and naturalness. However,…

cs.CL2025

Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems

Guojian Li, Chengyou Wang, Hongfei Xue +8

Full-duplex interaction is crucial for natural human-machine communication, yet remains challenging as it requires robust turn-taking detection to decide when the system should spe…

cs.SD2025

Towards Building Speech Large Language Models for Multitask Understanding in Low-Resource Languages

Mingchen Shao, Bingshen Mu, Chengyou Wang +4

Speech large language models (SLLMs) built on speech encoders, adapters, and LLMs demonstrate remarkable multitask understanding performance in high-resource languages such as Engl…