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Hui Wang

11 papers hereh-index 670 citations9 works total

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

author position
  • first author2
  • middle author8

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • eess.AS5
  • cs.CL3
  • cs.SD2
  • cs.LG1
same name
  • Hui Wang — 29 papers, h 21
  • Hui Wang — 29 papers, h 10
  • Hui Wang — 20 papers, h 11
  • Hui Wang — 16 papers, h 18
  • Hui Wang — 14 papers, h 5
  • Hui Wang — 13 papers, h 18

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.CL2026

NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities

Jagadeesh Balam, Travis Bartley, Edresson Casanova +52

We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combines a streaming speech encoder an…

cs.CL2026

Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction

Qi Chen, Yunfei Chu, Haolin He +15

Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather than carefully composed tex…

cs.SD2026

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Ziyang Ma, Zhikang Niu, Wenming Tu +30

We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To sup…

eess.AS2026

Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training

Yifan Yang, Bing Han, Hui Wang +8

Modeling fine-grained speaking styles remains challenging for language-speech representation pre-training, as existing speech-text models are typically trained with coarse captions…

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