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Xuelong Li

4 papers here

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

author position
  • last author4

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

fields
  • cs.CL3
  • cs.SD1
same name
  • Xuelong Li — 65 papers, h 135
  • Xuelong Li — 33 papers
  • Xuelong Li — 19 papers
  • Xuelong Li — 13 papers
  • Xuelong Li — 12 papers, h 21
  • Xuelong Li — 9 papers

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 citedGOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM

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

collaborators

4 papers

cs.CL2026

A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction

Qing Wang, Zehan Li, Yaodong Song +6

This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)…

cs.CL2025

GOAT-SLM: A Spoken Language Model with Paralinguistic and Speaker Characteristic Awareness

Hongjie Chen, Zehan Li, Yaodong Song +13

Recent advances in end-to-end spoken language models (SLMs) have significantly improved the ability of AI systems to engage in natural spoken interactions. However, most existing m…

cs.SD2025

DIFFA: Large Language Diffusion Models Can Listen and Understand

Jiaming Zhou, Hongjie Chen, Shiwan Zhao +9

Recent advances in large language models (LLMs) have shown remarkable capabilities across textual and multimodal domains. In parallel, diffusion-based language models have emerged…

cs.CL2025★ 1 cited

GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM

Yaodong Song, Hongjie Chen, Jie Lian +8

While large language models (LLMs) have revolutionized text-to-speech (TTS) synthesis through discrete tokenization paradigms, current architectures exhibit fundamental tensions be…

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