62 citations · 247 across the 32 of their papers we have counts for
13 papers · 1 filter
CHARM: Charge Calibration and Acoustic Rescue for LLM-based Multimodal Sarcasm Detection
Qiyang Sun, Yi Chang, Yupei Li +3
Sarcasm detection, the identification of discrepancies between literal and intended meaning, is a fundamental task in affective computing. However, zero-shot instruction-tuned Larg…
SIGMA: Saliency-Guided Sparse Mask Attacks for Speech Emotion Recognition
Qiyang Sun, Yi Chang, Zixing Zhang +1
Speech conveys rich emotional information. As Speech Emotion Recognition (SER) is usually deployed in privacy-sensitive and reliability-critical environments, adversarial attacks o…
AudioFab: Building A General and Intelligent Audio Factory through Tool Learning
Cheng Zhu, Jing Han, Qianshuai Xue +3
Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient f…
SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs
Zhongren Dong, Bin Wang, Jing Han +4
Neural Speech Codecs face a fundamental trade-off at low bitrates: preserving acoustic fidelity often compromises semantic richness. To address this, we introduce SACodec, a novel…
ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics over Acoustic Foundation Models
Zixing Zhang, Weixiang Xu, Zhongren Dong +5
Computational paralinguistics (ComParal) aims to develop algorithms and models to automatically detect, analyze, and interpret non-verbal information from speech communication, e.…
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition
Zixing Zhang, Zhongren Dong, Weixiang Xu +1
With the increasing implementation of machine learning models on edge or Internet-of-Things (IoT) devices, deploying advanced models on resource-constrained IoT devices remains cha…
Adaptive Speech Emotion Representation Learning Based On Dynamic Graph
Yingxue Gao, Huan Zhao, Zixing Zhang
Graph representation learning has become a hot research topic due to its powerful nonlinear fitting capability in extracting representative node embeddings. However, for sequential…
Intelligent Cardiac Auscultation for Murmur Detection via Parallel-Attentive Models with Uncertainty Estimation
Zixing Zhang, Tao Pang, Jing Han +1
Heart murmurs are a common manifestation of cardiovascular diseases and can provide crucial clues to early cardiac abnormalities. While most current research methods primarily focu…
HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech
Zhongren Dong, Zixing Zhang, Weixiang Xu +3
Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectu…
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
Yi Chang, Zhao Ren, Zixing Zhang +6
Speech contains rich information on the emotions of humans, and Speech Emotion Recognition (SER) has been an important topic in the area of human-computer interaction. The robustne…
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos +6
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challen…
Learning audio sequence representations for acoustic event classification
Zixing Zhang, Ding Liu, Jing Han +2
Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that captur…
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Zixing Zhang, Jürgen Geiger, Jouni Pohjalainen +3
Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge.…