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

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Showing 351–400 of 431 papers

TitleStatusHype
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition—0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
Cross-lingual and Multilingual Speech Emotion Recognition on English and French—0
Cross Lingual Cross Corpus Speech Emotion Recognition—0
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
Deep Residual Local Feature Learning for Speech Emotion Recognition—0
Deep scattering network for speech emotion recognition—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
Describing emotions with acoustic property prompts for speech emotion recognition—0
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP—0
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction—0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
Domain Adversarial for Acoustic Emotion Recognition—0
Double Multi-Head Attention Multimodal System for Odyssey 2024 Speech Emotion Recognition Challenge—0
DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—0
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]—0
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
Effect of different splitting criteria on the performance of speech emotion recognition—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations—0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks—0
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model—0
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network—0
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
Emotion Recognition In Persian Speech Using Deep Neural Networks—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
EMOVO Corpus: an Italian Emotional Speech Database—0
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs—0
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition—0
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition—0
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations—0
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings—0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition—0
Enhanced Speech Emotion Recognition with Efficient Channel Attention Guided Deep CNN-BiLSTM Framework—0
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning—0
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features—0
Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025—0
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech—0
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition—0
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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