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 151–200 of 431 papers

TitleStatusHype
A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition—0
Can Emotion Fool Anti-spoofing?—0
Adapting WavLM 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
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition—0
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms—0
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations—0
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network—0
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition—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
Biologically inspired speech emotion recognition—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
CAMEO: Collection of Multilingual Emotional Speech Corpora—0
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech—0
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition—0
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition—0
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model—0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus—0
Bimodal Speech Emotion Recognition Using Pre-Trained Language Models—0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks—0
Bimodal Connection Attention Fusion for Speech Emotion Recognition—0
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer—0
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network—0
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition—0
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition—0
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition—0
Forewords—0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?—0
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition—0
Beyond Isolated Utterances: Conversational Emotion Recognition—0
Show:102550
← PrevPage 4 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