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Zhe Li

6 papers hereh-index 326 citations14 works total

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

author position
  • first author1
  • middle author4

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.SD3
  • eess.AS2
  • cs.MM1
same name
  • Zhe Li — 39 papers, h 16
  • Zhe Li — 18 papers, h 19
  • Zhe Li — 16 papers, h 11
  • Zhe Li — 14 papers, h 7
  • Zhe Li — 13 papers, h 5
  • Zhe Li — 11 papers, h 9

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 2026Show all

4 papers · 1 filter

cs.SD2026

TS-SP: Learning Speaker-Preserving Representations in Audio Large Language Models

Junjie Li, Zheng Liang, Zhe Li +2

Audio large language models (ALLMs) can understand speech content, yet their ability to use speaker identity for verification remains limited. We propose TS-SP (Two-Stage Speaker P…

cs.SD2026

Beyond Residual Connections: Manifold-Constrained Hyper-Connections for Robust Speaker Representation Learning

Zezhong Jin, Xiaoyu Wang, Zhe Li +4

Residual connections are fundamental to deep speaker recogni- tion models, such as ECAPA-TDNN and ResNet. However, standard identity mapping limits information flow to a sin- gle p…

cs.MM2026

EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation

Zilong Huang, Kong Aik Lee, Junjie Li +2

Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC ofte…

eess.AS2026

UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition

Chong-Xin Gan, Peter Bell, Man-Wai Mak +4

The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm…

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