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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 201–250 of 431 papers

TitleStatusHype
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP—0
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks—0
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate—0
DWFormer: Dynamic Window transFormer for Speech Emotion RecognitionCode1
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech ProcessingCode1
A low latency attention module for streaming self-supervised speech representation learningCode0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
Audio Representation Learning by Distilling Video as Privileged Information—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
Modulation spectral features for speech emotion recognition using deep neural networks—0
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks—0
EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification MethodologiesCode1
A speech corpus of Quechua Collao for automatic dimensional emotion recognitionCode0
Large Raw Emotional Dataset with Aggregation MechanismCode1
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
Analysis of constant-Q filterbank based representations for speech emotion recognition—0
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text CorporaCode1
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion RecognitionCode1
Describing emotions with acoustic property prompts for speech emotion recognition—0
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
SPEAKER VGG CCT: Cross-corpus Speech Emotion Recognition with Speaker Embedding and Vision TransformersCode1
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition—0
GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion RecognitionCode1
Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured LearningCode0
Effect of different splitting criteria on the performance of speech emotion recognition—0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network—0
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution LearningCode0
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts—0
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring—0
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms—0
Representation Learning with Graph Neural Networks for Speech Emotion Recognition—0
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition—0
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text—0
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-DistributionsCode0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
Semi-supervised cross-lingual speech emotion recognition—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
A cross-corpus study on speech emotion recognition—0
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition—0
SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning—0
Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers—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