activity
20242026
most citedA Survey on Speech Large Language Models for Understanding

6 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

eess.AS2026

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…

eess.AS2025

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…

eess.AS2025

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…

eess.AS2025

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…

eess.AS2025

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…

cs.CL2025

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…