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eess.AS2026

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…

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

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…