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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 101–150 of 431 papers

TitleStatusHype
Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive MixupCode0
Learning Speech Emotion Representations in the Quaternion DomainCode0
Learning Alignment for Multimodal Emotion Recognition from SpeechCode0
Learning Rate CurriculumCode0
Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured LearningCode0
Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from SpeechCode0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
Attention Based Fully Convolutional Network for Speech Emotion RecognitionCode0
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-DistributionsCode0
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion RecognitionCode0
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion RecognitionCode0
Iterative Feature Boosting for Explainable Speech Emotion RecognitionCode0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Deep Learning based Emotion Recognition System Using Speech Features and TranscriptionsCode0
An Interaction-aware Attention Network for Speech Emotion Recognition in Spoken DialogsCode0
INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion RecognitionCode0
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking ModelCode0
An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data AugmentationCode0
Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data SelectionCode0
The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation SystemsCode0
Audio Explanation Synthesis with Generative Foundation ModelsCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Integrating Recurrence Dynamics for Speech Emotion RecognitionCode0
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion RecognitionCode0
Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion RecognitionCode0
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition PerformanceCode0
Filter-based multi-task cross-corpus feature learning for speech emotion recognitionCode0
Efficient Arabic emotion recognition using deep neural networksCode0
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech RecognitionCode0
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask LearningCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Enrolment-based personalisation for improving individual-level fairness in speech emotion recognitionCode0
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in ConversationCode0
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from SpeechCode0
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural NetworksCode0
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic FeaturesCode0
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution LearningCode0
Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention MechanismCode0
Speech Emotion Recognition Using Multi-hop Attention MechanismCode0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Fine-grained Speech Sentiment Analysis in Chinese Psychological Support Hotlines Based on Large-scale Pre-trained ModelCode0
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion DatasetsCode0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Domain Adversarial for Acoustic Emotion Recognition—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT—0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction—0
Audio Representation Learning by Distilling Video as Privileged Information—0
An analysis of large speech models-based representations for 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