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 251–300 of 431 papers

TitleStatusHype
Visually Guided Self Supervised Learning of Speech Representations—0
WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition—0
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations—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
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus—0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer—0
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition—0
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition—0
Forewords—0
FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion 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
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
HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition—0
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition—0
Improved Frame Level Features and SVM Supervectors Approach for the Recogniton of Emotional States from Speech: Application to categorical and dimensional states—0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM—0
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation—0
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms—0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation—0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition—0
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis—0
Investigating the Impact of Word Informativeness on Speech Emotion Recognition—0
Investigations on Audiovisual Emotion Recognition in Noisy Conditions—0
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition—0
A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features—0
Fine-grained Early Frequency Attention for Deep Speaker Representation Learning—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
learning discriminative features from spectrograms using center loss for speech emotion recognition—0
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition—0
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech—0
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition—0
Learning spectro-temporal features with 3D CNNs for speech emotion recognition—0
Learning Spontaneity to Improve Emotion Recognition In Speech—0
Learning Transferable Features for Speech Emotion Recognition—0
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks—0
MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable 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