1 citations · 1 across the 11 of their papers we have counts for
16 papers
RefTAG: Reflection-aware Temporal Audio Grounding in Large Audio Language Models
Yuhang Dai, Xin Shu, Zhennan Lin +5
Temporal audio grounding requires locating every occurrence of a queried target, making both occurrence counts and interval boundaries essential. We propose RefTAG, a post-training…
Bridging the Modality Gap in Long-Form Clinical Audio: A Comparative Study of Lightweight and Heavyweight End-to-End SOAP Generation
Ziyu Zhang, Mingchen Shao, Wenjie Tian +3
Automating clinical documentation from long-form doctor-patient conversations remains challenging for modern audio-language models. While cascaded ASR systems perform well, end-to-…
Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech
Tianlun Zuo, Ziyu Zhang, Tingzhi Mao +2
Low-resource multilingual text-to-speech (TTS) systems have expanded language coverage, but their robustness under complex text inputs remains insufficiently diagnosed. Existing ev…
NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding
Yuang Cao, Bingshen Mu, Zhennan Lin +7
Human speech includes nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, which convey affective and interactional information. Existing approaches typica…
TAG-Bench: Benchmarking Temporal Audio Grounding in Large Audio Language Models
Yuhang Dai, Xin Shu, Zengxi Li +3
Large audio language models (LALMs) can describe what is heard, but their ability to localize when queried content occurs remains less systematically evaluated. We present TAG-Benc…
The SLT 2026 SmartGlasses Challenge: Benchmarking Egocentric Multi-Talker Speech Recognition and Understanding with Audio-Language Models
Dehui Gao, Zhixian Zhao, Zhennan Lin +14
Recent advances in large language models (LLMs) and multimodal LLMs (MLLMs) have created new opportunities for wearable speech interfaces, with smart glasses providing an egocentri…
MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios
Zhaokai Sun, Shuai Wang, Zhennan Lin +6
Spoken Language Understanding (SLU) is moving from task-specific pipelines toward large audio language models (LALMs) that generate natural-language responses. However, existing sp…
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,…
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…
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…
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,…
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…
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…
Summary on The Multilingual Conversational Speech Language Model Challenge: Datasets, Tasks, Baselines, and Methods
Bingshen Mu, Pengcheng Guo, Zhaokai Sun +8
This paper summarizes the Interspeech2025 Multilingual Conversational Speech Language Model (MLC-SLM) challenge, which aims to advance the exploration of building effective multili…
OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue
Xuelong Geng, Qijie Shao, Hongfei Xue +20
Empathy is crucial in enabling natural interactions within spoken dialogue systems, allowing machines to recognize and respond appropriately to paralinguistic cues such as age, gen…
MSU-Bench: Towards Understanding the Conversational Multi-talker Scenarios
Shuai Wang, Zhaokai Sun, Zhennan Lin +3
Spoken Language Understanding (SLU) has progressed from traditional single-task methods to large audio language model (LALM) solutions. Yet, most existing speech benchmarks focus o…