6 citations · 6 across the 17 of their papers we have counts for
18 papers
SURE-EVAL: A Systematic and Unified Agentic Framework for Reproducible Evaluation
Jing Peng, Junhao Du, Yixuan Wang +21
Audio and speech models are released rapidly, but reported scores often conflate model capability with deployment and evaluation choices. The same checkpoint can produce different…
dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model
Hankun Wang, Bohan Li, Shi Lian +7
Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface fo…
Making AI Scientists Auditable from Evidence to Claim
Zijian Wang, Hanqi Li, Ziyue Yang +17
AI research systems can report improved results without establishing that the tested implementation matches the method described in their conclusions. We introduce Xcientist, a res…
XFlow: An Executable Protocol Programming System for Reliable Multi-Agent Workflows
Hanqi Li, Jing Peng, Zijian Wang +2
LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited. A central source of this limitati…
VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track
Wenming Tu, Jian Gao, Yanru Huo +9
Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or capt…
A Unified and Reproducible Experimentation Framework for Speech Understanding
Jing Peng, Junhao Du, Chenghao Wang +21
Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched…
Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Yucheng Wang, Jing Peng, Hanqi Li +6
Audio agents extend large audio-language models (LALMs) by decomposing audio questions into tool calls, intermediate evidence, and iterative reasoning steps. However, as LALMs beco…
TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs
Jing Peng, Chenghao Wang, Yi Yang +5
Speech LLM post-training increasingly relies on efficient cross-modal alignment and robust low-resource adaptation, yet collecting large-scale audio-text pairs remains costly. Text…
RAS: a Reliability Oriented Metric for Automatic Speech Recognition
Wenbin Huang, Yuhang Qiu, Bohan Li +5
Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading for both users and downstream…
G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition
Jing Peng, Ziyi Chen, Haoyu Li +7
We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting, chunk-wise inference must preserve me…
TC-BiMamba: Trans-Chunk bidirectionally within BiMamba for unified streaming and non-streaming ASR
Qingshun She, Jing Peng, Yangui Fang +2
This work investigates bidirectional Mamba (BiMamba) for unified streaming and non-streaming automatic speech recognition (ASR). Dynamic chunk size training enables a single model…
TASU: Text-Only Alignment for Speech Understanding
Jing Peng, Yi Yang, Xu Li +5
Recent advances in Speech Large Language Models (Speech LLMs) have paved the way for unified architectures across diverse speech understanding tasks. However, prevailing alignment…
ISA-Bench: Benchmarking Instruction Sensitivity for Large Audio Language Models
Bohan Li, Wenbin Huang, Yuhang Qiu +7
Large Audio Language Models (LALMs), which couple acoustic perception with large language models (LLMs) to extract and understand diverse information from audio, have attracted int…
Joint decoding method for controllable contextual speech recognition based on Speech LLM
Yangui Fang, Jing Peng, Yu Xi +5
Contextual speech recognition refers to the ability to identify preferences for specific content based on contextual information. Recently, leveraging the contextual understanding…
MOSA: Mixtures of Simple Adapters Outperform Monolithic Approaches in LLM-based Multilingual ASR
Junjie Li, Jing Peng, Yangui Fang +2
LLM-based ASR overcomes multilingual data scarcity by projecting speech representations into the LLM space to leverage its robust semantic and reasoning capabilities. However, whil…
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Yangui Fang, Jing Peng, Xu Li +4
Recent advances in automatic speech recognition (ASR) have combined speech encoders with large language models (LLMs) through projection, forming Speech LLMs with strong performanc…
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction
Yangui Fang, Baixu Chen, Jing Peng +4
Automatic Speech Recognition (ASR) error correction aims to correct recognition errors while preserving accurate text. Although traditional approaches demonstrate moderate effectiv…
A Survey on Speech Large Language Models for Understanding
Jing Peng, Yucheng Wang, Bohan Li +9
Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that…