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

Showing 1120 of 431 papers

TitleStatusHype
AST: Audio Spectrogram TransformerCode2
Steering Language Model to Stable Speech Emotion Recognition via Contextual Perception and Chain of ThoughtCode1
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion RecognitionCode1
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion RecognitionCode1
Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and ResultsCode1
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transferCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention MechanismCode1
Frame-level emotional state alignment method for speech emotion recognitionCode1
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Vertically long patch ViTAccuracy94.07Unverified
2ConformerXL-PAccuracy88.2Unverified
3CoordViTAccuracy82.96Unverified
4SepTr + LeRaCAccuracy70.95Unverified
5SepTrAccuracy70.47Unverified
6ResNet-18 + SPELAccuracy68.12Unverified
7ViTAccuracy67.81Unverified
8ResNet-18 + PyNADAAccuracy65.15Unverified
9GRUAccuracy55.01Unverified
#ModelMetricClaimedVerifiedStatus
1SER with MTLUA CV0.78Unverified
2emoDARTSUA CV0.77Unverified
3LSTM+FCWA0.76Unverified
4TAPWA CV0.74Unverified
5SYSCOMB: BLSTMATT with CSA (session5)UA0.74Unverified
6Partially Fine-tuned HuBERT LargeWA CV0.73Unverified
7CNN - DARTSUA0.7Unverified
8CNN+LSTMUA0.65Unverified
#ModelMetricClaimedVerifiedStatus
1VQ-MAE-S-12 (Frame) + Query2EmoAccuracy84.1Unverified
2CNN-X (Shallow CNN)Accuracy82.99Unverified
3xlsr-Wav2Vec2.0(FineTuning)Accuracy81.82Unverified
4CNN-14 (Fine-Tuning)Accuracy76.58Unverified
5AlexNet (FineTuning)Accuracy61.67Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.76Unverified
2wavlmCCC0.75Unverified
3w2v2-L-robust-12CCC0.75Unverified
4preCPCCCC0.71Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.68Unverified
2wavlmCCC0.67Unverified
3w2v2-L-robust-12CCC0.66Unverified
4preCPCCCC0.64Unverified
#ModelMetricClaimedVerifiedStatus
1wav2small-TeacherCCC0.68Unverified
2wavlmCCC0.65Unverified
3w2v2-L-robust-12CCC0.64Unverified
4preCPCCCC0.38Unverified
#ModelMetricClaimedVerifiedStatus
1DAWN-hidden-SVMUnweighted Accuracy (UA)32.1Unverified
2Wav2Small-VAD-SVMUnweighted Accuracy (UA)23.3Unverified
3Speechbrain Wav2Vec2Unweighted Accuracy (UA)20.7Unverified
#ModelMetricClaimedVerifiedStatus
1emotion2vec+baseWeighted Accuracy (WA)79.4Unverified
2emotion2vec+largeWeighted Accuracy (WA)69.5Unverified
3emotion2vecWeighted Accuracy (WA)64.75Unverified
#ModelMetricClaimedVerifiedStatus
1Dusha baselineMacro F10.77Unverified
#ModelMetricClaimedVerifiedStatus
1Dusha baselineMacro F10.54Unverified
#ModelMetricClaimedVerifiedStatus
1VGG-optiVMD1:1 Accuracy96.09Unverified
#ModelMetricClaimedVerifiedStatus
1VQ-MAE-S-12 (Frame) + Query2EmoAccuracy90.2Unverified
#ModelMetricClaimedVerifiedStatus
1PyResNetUnweighted Accuracy (UA)0.43Unverified
#ModelMetricClaimedVerifiedStatus
1emoDARTSUA0.66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCCC (Arousal)0.76Unverified
#ModelMetricClaimedVerifiedStatus
1CNN (1D)Unweighted Accuracy65.2Unverified