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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 51–100 of 431 papers

TitleStatusHype
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
A Survey on Speech Large Language Models—0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition—0
SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning—0
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features—0
Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?—0
Audio Explanation Synthesis with Generative Foundation ModelsCode0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
Multi-Scale Temporal Transformer For Speech Emotion Recognition—0
Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data SelectionCode0
Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance—0
Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers—0
TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion RecognitionCode0
Turbo your multi-modal classification with contrastive learning—0
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic FeaturesCode0
Leveraging Content and Acoustic Representations for Speech Emotion RecognitionCode0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition—0
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional VariabilityCode0
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion RecognitionCode1
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Conditioning LLMs with Emotion in Neural Machine Translation—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations—0
PCQ: Emotion Recognition in Speech via Progressive Channel Querying—0
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion RecognitionCode0
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition—0
A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition—0
Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion RecognitionCode0
Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation—0
Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and ResultsCode1
Double Multi-Head Attention Multimodal System for Odyssey 2024 Speech Emotion Recognition Challenge—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
What Does it Take to Generalize SER Model Across Datasets? A Comprehensive Benchmark—0
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask LearningCode0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion DatasetsCode0
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and BenchmarkCode3
Enrolment-based personalisation for improving individual-level fairness in speech emotion recognitionCode0
INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion RecognitionCode0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
BLSP-Emo: Towards Empathetic Large Speech-Language ModelsCode2
Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture—0
Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion RecognitionCode0
1st Place Solution to Odyssey Emotion Recognition Challenge Task1: Tackling Class Imbalance Problem—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