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Longyue Wang

40 papers hereh-index 6210 citations52 works total

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

author position
  • middle author37

Across the 37 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CL25
  • cs.AI6
  • cs.SD4
  • cs.CV3
  • cs.CR1
  • cs.LG1
same name
  • Longyue Wang — 15 papers, h 9
  • Longyue Wang — 8 papers, h 8
  • Longyue Wang — 5 papers, h 26
  • Longyue Wang — 3 papers, h 17
  • Longyue Wang — 2 papers, h 3
  • Longyue Wang — 2 papers, h 2

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

collaborators
Showing cs.SDShow all

4 papers · 1 filter

cs.SD2026

The Affective Bridge: Preserving Speech Representations while Enhancing Deepfake Detection vian emotional Constraints

Yupei Li, Chenyang Lyu, Longyue Wang +4

Speech deepfake detection (DFD) has benefited from diverse acoustic and semantic speech representations, many of which encode valuable speech information and are costly to train. P…

cs.SD2026

LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech

Fei Yang, Xuanfan Ni, Renyi Yang +7

Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription,…

cs.SD2025

Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation

Xuanfan Ni, Fei Yang, Fengping Tian +6

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mi…

cs.SD2025

MECap-R1: Emotion-aware Policy with Reinforcement Learning for Multimodal Emotion Captioning

Haoqin Sun, Chenyang Lyu, Xiangyu Kong +9

Speech Emotion Captioning (SEC) has emerged as a notable research direction. The inherent complexity of emotional content in human speech makes it challenging for traditional discr…

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