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Jun Zhang

8 papers hereh-index 6213 citations12 works total

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

author position
  • middle author6

Across the 6 of 8 papers where every author was matched, so the position is known.

fields
  • eess.AS3
  • cs.CL2
  • astro-ph.GA1
  • cs.AI1
  • cs.SD1
same name
  • Jun Zhang — 25 papers, h 9
  • Jun Zhang — 17 papers, h 7
  • Jun Zhang — 15 papers, h 7
  • Jun Zhang — 13 papers, h 2
  • Jun Zhang — 11 papers, h 4
  • Jun Zhang — 9 papers, h 3

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

activity
20242026
collaborators
Showing eess.ASShow all

4 papers · 1 filter

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.AS2025

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…

eess.AS2024

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…

eess.AS2024

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…

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