1 citations · 4 across the 11 of their papers we have counts for
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SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless…
Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation
Siyin Wang, Wenyi Yu, Yudong Yang +10
Speech quality assessment typically requires evaluating audio from multiple aspects, such as mean opinion score (MOS) and speaker similarity (SIM) \etc., which can be challenging t…
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR
Van Tung Pham, Yist Lin, Tao Han +4
Recent works have shown promising results in connecting speech encoders to large language models (LLMs) for speech recognition. However, several limitations persist, including limi…
Can Large Language Models Understand Spatial Audio?
Changli Tang, Wenyi Yu, Guangzhi Sun +8
This paper explores enabling large language models (LLMs) to understand spatial information from multichannel audio, a skill currently lacking in auditory LLMs. By leveraging LLMs'…
Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR
Yerbolat Khassanov, Zhipeng Chen, Tianfeng Chen +5
This paper addresses challenges in integrating new languages into a pre-trained multilingual automatic speech recognition (mASR) system, particularly in scenarios where training da…
SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words
Junyi Ao, Yuancheng Wang, Xiaohai Tian +6
Speech encompasses a wealth of information, including but not limited to content, paralinguistic, and environmental information. This comprehensive nature of speech significantly i…