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

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Showing 301–350 of 431 papers

TitleStatusHype
Self-supervised Graphs for Audio Representation Learning with Limited Labeled DataCode0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—0
A study on cross-corpus speech emotion recognition and data augmentation—0
A New Amharic Speech Emotion Dataset and Classification Benchmark—0
Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition—0
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition—0
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition—0
A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features—0
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—0
Biologically inspired speech emotion recognition—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings—0
Multistage linguistic conditioning of convolutional layers for speech emotion recognition—0
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural NetworksCode0
Speech Emotion Recognition Based on CNN+LSTM Model—0
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition—0
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition—0
FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition—0
Beyond Isolated Utterances: Conversational Emotion Recognition—0
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network—0
DeepEMO: Deep Learning for Speech Emotion RecognitionCode0
Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo MultilabelCode0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
The Role of Phonetic Units in Speech Emotion Recognition—0
An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data AugmentationCode0
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
An Attribute-Aligned Strategy for Learning Speech Representation—0
Deep scattering network for speech emotion recognition—0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Speaker Attentive Speech Emotion Recognition—0
Unsupervised low-rank representations for speech emotion recognition—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning—0
Self-paced ensemble learning for speech and audio classification—0
Investigations on Audiovisual Emotion Recognition in Noisy Conditions—0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
Non-linear frequency warping using constant-Q transformation for speech emotion recognition—0
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation—0
Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion RecognitionCode0
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion RecognitionCode0
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network—0
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition—0
Convolutional and Recurrent Neural Networks for Spoken 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