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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 151–200 of 431 papers

TitleStatusHype
An analysis of large speech models-based representations for speech emotion recognition—0
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion RecognitionCode0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks—0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation—0
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTCode2
Prompting Audios Using Acoustic Properties For Emotion Representation—0
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains—0
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition—0
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement—0
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition—0
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations—0
Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion RecognitionCode0
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition—0
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio DetectionCode1
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations—0
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques—0
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion RecognitionCode1
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality ConversionCode0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
Vesper: A Compact and Effective Pretrained Model for Speech Emotion RecognitionCode1
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition—0
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition—0
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion RecognitionCode1
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
Speech Emotion Diarization: Which Emotion Appears When?Code1
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition—0
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus—0
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks—0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition—0
Transfer Learning for Personality Perception via Speech Emotion Recognition—0
Enhancing Speech Emotion Recognition Through Differentiable Architecture SearchCode1
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition—0
Versatile audio-visual learning for emotion recognition—0
Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive MixupCode0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition—0
A vector quantized masked autoencoder for speech emotion recognitionCode1
An Empirical Study and Improvement 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