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20172026
most citedSpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing

90 citations · 282 across the 39 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CL2024★ 1 cited

PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling

Haojie Xie, Yirong Chen, Xiaofen Xing +2

Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue wher…

cs.LG2024★ 1 cited

Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification

Huachao Yan, Kailing Guo, Shiwei Song +4

Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existi…

cs.AI2024

SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation

Huimin Zheng, Xiaofeng Xing, Xiangmin Xu

We present a novel approach to personalized sleep health management using few-shot Chain-of-Thought (CoT) distillation, enabling small-scale language models (> 2B parameters) to ri…

eess.AS2024

Multi-Scale Temporal Transformer For Speech Emotion Recognition

Zhipeng Li, Xiaofen Xing, Yuanbo Fang +3

Speech emotion recognition plays a crucial role in human-machine interaction systems. Recently various optimized Transformers have been successfully applied to speech emotion recog…

eess.AS2024

AS-Speech: Adaptive Style For Speech Synthesis

Zhipeng Li, Xiaofen Xing, Jun Wang +4

In recent years, there has been significant progress in Text-to-Speech (TTS) synthesis technology, enabling the high-quality synthesis of voices in common scenarios. In unseen situ…

cs.CV2024★ 1 cited

VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool

Yan Wang, Yawen Zeng, Jingsheng Zheng +3

Multimodal large language models (MLLMs) are flourishing, but mainly focus on images with less attention than videos, especially in sub-fields such as prompt engineering, video cha…