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Jun Zhang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • eess.AS3
  • cs.CL1
same name
  • Jun Zhang — 54 papers, h 58
  • Jun Zhang — 42 papers, h 36
  • Jun Zhang — 39 papers
  • Jun Zhang — 31 papers, h 33
  • Jun Zhang — 16 papers
  • Jun Zhang — 14 papers, h 20

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedSALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation

1 citations · 2 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2025

SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversation

Wenyi Yu, Siyin Wang, Xiaoyu Yang +7

In order to enable fluid and natural human-machine speech interaction, existing full-duplex conversational systems often adopt modular architectures with auxiliary components such…

eess.AS2025★ 1 cited

Solla: Towards a Speech-Oriented LLM That Hears Acoustic Context

Junyi Ao, Dekun Chen, Xiaohai Tian +6

Large Language Models (LLMs) have recently shown remarkable ability to process not only text but also multimodal inputs such as speech and audio. However, most existing models prim…

eess.AS2025

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

Siyin Wang, Wenyi Yu, Xianzhao Chen +7

This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical…

eess.AS2024★ 1 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.