1 citations · 1 across the 7 of their papers we have counts for
8 papers
The NVVSpeech Challenge 2026: Overview and Lessons for Speech Understanding and Generation with Nonverbal Vocalizations
Liumeng Xue, Yuang Cao, Jingbin Hu +12
Nonverbal vocalizations (NVVs), such as laughter and sighs, convey meaning alongside words. NVV-aware speech systems must recognize and generate these events while preserving lexic…
Preference Optimization with LALM Feedback for Continuous Autoregressive Non-Verbal Vocalization Generation
Jingbin Hu, Qirui Zhan, Yuang Cao +8
We propose a preference optimization framework with Large Audio-Language Model (LALM) feedback for controllable non-verbal vocalization (NVV) generation in continuous autoregressiv…
NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding
Yuang Cao, Bingshen Mu, Zhennan Lin +7
Human speech includes nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, which convey affective and interactional information. Existing approaches typica…
Source-Adaptive Data Curation for Bilingual NVV-Aware ASR
Yuang Cao, Qirui Zhan, Jingbin Hu +8
Nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, convey affective and interactional information that conventional automatic speech recognition (ASR) sy…
MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios
Zhaokai Sun, Shuai Wang, Zhennan Lin +6
Spoken Language Understanding (SLU) is moving from task-specific pipelines toward large audio language models (LALMs) that generate natural-language responses. However, existing sp…
Towards Fine-Grained Multi-Dimensional Speech Understanding: Data Pipeline, Benchmark, and Model
Guojian Li, Zhixian Zhao, Zhennan Lin +9
While speech Large Language Models (LLMs) excel at conventional tasks like basic speech recognition, they lack fine-grained, multi-dimensional perception. This deficiency is eviden…
OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue
Xuelong Geng, Qijie Shao, Hongfei Xue +20
Empathy is crucial in enabling natural interactions within spoken dialogue systems, allowing machines to recognize and respond appropriately to paralinguistic cues such as age, gen…
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
Xuelong Geng, Kun Wei, Qijie Shao +18
Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable compreh…