11 citations · 13 across the 6 of their papers we have counts for
7 papers
Full-Duplex Speech Models Take the Floor When Asked, Not When Needed
Linkai Peng, Baorian Nuchged, Kaiqi Fu +1
Full-duplex speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners speak when addressed or when t…
Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models
Linkai Peng, Baorian Nuchged
Speech language models are increasingly evaluated on paralinguistic tasks by the accuracy of prompted answers, but answer accuracy combines failures at different stages of the audi…
Represented but Ignored: A Causal Account of Prosodic Underuse in Audio-Language Models
Linkai Peng, Baorian Nuchged
Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content. A capable large audio-language model (audio-LLM) should th…
Pronunciation Assessment with Multi-modal Large Language Models
Kaiqi Fu, Linkai Peng, Nan Yang +1
Large language models (LLMs), renowned for their powerful conversational abilities, are widely recognized as exceptional tools in the field of education, particularly in the contex…
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
Ziqi Huang, Li Lin, Pujin Cheng +2
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR…
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Linkai Peng, Li Lin, Pujin Cheng +2
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of th…
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
Linkai Peng, Li Lin, Pujin Cheng +2
Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain…