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 251–300 of 431 papers

TitleStatusHype
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
AHD ConvNet for Speech Emotion Classification—0
SyntAct: A Synthesized Database of Basic Emotions—0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning—0
Learning Rate CurriculumCode0
Emotion Recognition In Persian Speech Using Deep Neural Networks—0
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction—0
Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature RepresentationCode1
Learning Speech Emotion Representations in the Quaternion DomainCode0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Neural Architecture Search for Speech Emotion Recognition—0
MMER: Multimodal Multi-task Learning for Speech Emotion RecognitionCode1
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition—0
Speech Emotion Recognition with Co-Attention based Multi-level Acoustic InformationCode1
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
A Dataset for Speech Emotion Recognition in Greek Theatrical PlaysCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
SepTr: Separable Transformer for Audio Spectrogram ProcessingCode1
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-LabelingCode1
Dawn of the transformer era in speech emotion recognition: closing the valence gapCode2
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition—0
Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition—0
Speech Emotion Recognition using Self-Supervised Features—0
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated LearningCode1
Speaker Normalization for Self-supervised Speech Emotion Recognition—0
Self-supervised Graphs for Audio Representation Learning with Limited Labeled DataCode0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—0
A study on cross-corpus speech emotion recognition and data augmentation—0
A New Amharic Speech Emotion Dataset and Classification Benchmark—0
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS datasetCode1
Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition—0
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition—0
Representation learning through cross-modal conditional teacher-student training for 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
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—0
Biologically inspired speech emotion recognition—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings—0
Multistage linguistic conditioning of convolutional layers for speech emotion recognition—0
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitionCode1
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED DatasetCode1
Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognitionCode1
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural NetworksCode0
Show:102550
← PrevPage 6 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