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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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MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition
nEMO: Dataset of Emotional Speech in Polish
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer
Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
EMOVOME: A Dataset for Emotion Recognition in Spontaneous Real-Life Speech
SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech
Mixer is more than just a model
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
Persian Speech Emotion Recognition by Fine-Tuning Transformers
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification
A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions
Frame-level emotional state alignment method for speech emotion recognition
DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models
Speech and Text-Based Emotion Recognizer
Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.