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Xianghu Yue

Xianghu Yue has 2 papers in our library, in 2026, most of them on Speech & spoken language, Evaluation & benchmarks; often with Yiming Chen, Chen Zhang, Haizhou Li.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 9 · 44 works · 749 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 65 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8470

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Tianjin UniversityTianjin · China2025–2026 · 13 papersEmployment since 2025current🎓National University of SingaporeSingapore · Singapore2019–2025 · 13 papers🎓Beijing Institute of TechnologyBeijing · China2017–2019 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Speech & spoken language · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. VoiceBench: Benchmarking LLM-Based Voice Assistants (2026 · 74 citations)
    Voice assistants built on audio-capable LLMs, such as GPT-4o-style systems, have had no common yardstick. Earlier work mostly reported ASR scores, anecdotal examples, or small spoken QA sets made from clean TTS audio. The authors address this with VoiceBench, an evaluation suite of 6,783 spoken instructions in eight…
  2. HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026)
    Large Audio-Language Models (LALMs) often make claims that the input audio does not support. Hallucination research has mostly targeted text and images, and earlier audio efforts such as AHa-Bench are small and mostly binary. The authors build HalluAudio, a benchmark covering speech, environmental sound and music…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. VoiceBench: Benchmarking LLM-Based Voice AssistantsTransactions of the Association for Computational Linguistics (TACL)Yiming Chen, Xianghu Yue, Chen Zhang, et al.2026 · 74 citations
  2. Multi-Graph Decoding for Code-Switching ASR ↗arXiv:1906.07523 · no summary yetEmre Yılmaz et al.2019 · 12 citations
  3. HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Feiyu Zhao, Yiming Chen, Wenhuan Lu, et al.2026

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 65 times (of the 149,439 papers whose bibliographies we read); 24 citing papers with a page here →.

Updated

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