SOTAVerified

Speech Emotion Recognition

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 431 papers

TitleStatusHype
Prompting Audios Using Acoustic Properties For Emotion Representation—0
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains—0
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition—0
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement—0
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition—0
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations—0
Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion RecognitionCode0
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition—0
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations—0
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques—0
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality ConversionCode0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition—0
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition—0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus—0
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition—0
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks—0
Transfer Learning for Personality Perception via Speech Emotion Recognition—0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition—0
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition—0
Versatile audio-visual learning for emotion recognition—0
Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive MixupCode0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition—0
An Empirical Study and Improvement for Speech Emotion Recognition—0
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP—0
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks—0
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate—0
A low latency attention module for streaming self-supervised speech representation learningCode0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Audio Representation Learning by Distilling Video as Privileged Information—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
Modulation spectral features for speech emotion recognition using deep neural networks—0
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks—0
A speech corpus of Quechua Collao for automatic dimensional emotion recognitionCode0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Show:102550
← PrevPage 5 of 9Next →
All datasetsCREMA-DIEMOCAPRAVDESSMSP-Podcast (Activation)MSP-Podcast (Dominance)MSP-Podcast (Valence)BERStRESDDusha CrowdDusha PodcastEMODBEmoDB Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Vertically long patch ViTAccuracy94.07—Unverified
2ConformerXL-PAccuracy88.2—Unverified
3CoordViTAccuracy82.96—Unverified
4SepTr + LeRaCAccuracy70.95—Unverified
5SepTrAccuracy70.47—Unverified
6ResNet-18 + SPELAccuracy68.12—Unverified
7ViTAccuracy67.81—Unverified
8ResNet-18 + PyNADAAccuracy65.15—Unverified
9GRUAccuracy55.01—Unverified
#ModelMetricClaimedVerifiedStatus
1SER with MTLUA CV0.78—Unverified
2emoDARTSUA CV0.77—Unverified
3LSTM+FCWA0.76—Unverified
4TAPWA CV0.74—Unverified
5SYSCOMB: BLSTMATT with CSA (session5)UA0.74—Unverified
6Partially Fine-tuned HuBERT LargeWA CV0.73—Unverified
7CNN - DARTSUA0.7—Unverified
8CNN+LSTMUA0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1VQ-MAE-S-12 (Frame) + Query2EmoAccuracy84.1—Unverified
2CNN-X (Shallow CNN)Accuracy82.99—Unverified
3xlsr-Wav2Vec2.0(FineTuning)Accuracy81.82—Unverified
4CNN-14 (Fine-Tuning)Accuracy76.58—Unverified
5AlexNet (FineTuning)Accuracy61.67—Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.76—Unverified
2wavlmCCC0.75—Unverified
3w2v2-L-robust-12CCC0.75—Unverified
4preCPCCCC0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.68—Unverified
2wavlmCCC0.67—Unverified
3w2v2-L-robust-12CCC0.66—Unverified
4preCPCCCC0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.68—Unverified
2wavlmCCC0.65—Unverified
3w2v2-L-robust-12CCC0.64—Unverified
4preCPCCCC0.38—Unverified
#ModelMetricClaimedVerifiedStatus
1DAWN-hidden-SVMUnweighted Accuracy (UA)32.1—Unverified
2Wav2Small-VAD-SVMUnweighted Accuracy (UA)23.3—Unverified
3Speechbrain Wav2Vec2Unweighted Accuracy (UA)20.7—Unverified
#ModelMetricClaimedVerifiedStatus
1emotion2vec+baseWeighted Accuracy (WA)79.4—Unverified
2emotion2vec+largeWeighted Accuracy (WA)69.5—Unverified
3emotion2vecWeighted Accuracy (WA)64.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Dusha baselineMacro F10.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Dusha baselineMacro F10.54—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG-optiVMD1:1 Accuracy96.09—Unverified
#ModelMetricClaimedVerifiedStatus
1VQ-MAE-S-12 (Frame) + Query2EmoAccuracy90.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PyResNetUnweighted Accuracy (UA)0.43—Unverified
#ModelMetricClaimedVerifiedStatus
1emoDARTSUA0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCCC (Arousal)0.76—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN (1D)Unweighted Accuracy65.2—Unverified