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Speech Emotion Recognition
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Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.
For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP
논문
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MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition
EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition
Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025
Towards Machine Unlearning for Paralinguistic Speech Processing
Investigating the Impact of Word Informativeness on Speech Emotion Recognition
Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge
Can Emotion Fool Anti-spoofing?
EmoSphere-SER: Enhancing Speech Emotion Recognition Through Spherical Representation with Auxiliary Classification
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge
Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning
Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach
CAMEO: Collection of Multilingual Emotional Speech Corpora
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets
Heterogeneous bimodal attention fusion for speech emotion recognition
Bimodal Connection Attention Fusion for Speech Emotion Recognition
Steering Language Model to Stable Speech Emotion Recognition via Contextual Perception and Chain of Thought
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model
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