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 301–350 of 431 papers

TitleStatusHype
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning—0
Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning—0
Meta Transfer Learning for Emotion Recognition—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition—0
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention—0
Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach—0
Mixer is more than just a model—0
Modulation spectral features for speech emotion recognition using deep neural networks—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition—0
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition—0
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition—0
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture—0
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—0
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text—0
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations—0
Multi-Scale Temporal Transformer For Speech Emotion Recognition—0
Multistage linguistic conditioning of convolutional layers for speech emotion recognition—0
Multi-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition—0
Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers—0
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
Multi-Window Data Augmentation Approach for Speech Emotion Recognition—0
Neural Architecture Search for Speech Emotion Recognition—0
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement—0
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
Non-linear frequency warping using constant-Q transformation for speech emotion recognition—0
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition—0
Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition—0
Once More, With Feeling: Measuring Emotion of Acting Performances in Contemporary American Film—0
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—0
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks—0
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition—0
Optimizing Speech Emotion Recognition using Manta-Ray Based Feature Selection—0
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation—0
Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition—0
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition—0
PCQ: Emotion Recognition in Speech via Progressive Channel Querying—0
Persian Speech Emotion Recognition by Fine-Tuning Transformers—0
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition—0
Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers—0
Pitch-Synchronous Single Frequency Filtering Spectrogram for Speech Emotion Recognition—0
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning—0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Enrolment-based personalisation for improving individual-level fairness in speech emotion recognitionCode0
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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