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
SERAB: A multi-lingual benchmark for speech emotion recognitionCode1
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
DeepEMO: Deep Learning for Speech Emotion RecognitionCode0
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network—0
Speech Emotion Recognition with Multi-Task LearningCode1
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
Efficient Speech Emotion Recognition Using Multi-Scale CNN and AttentionCode1
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
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
AST: Audio Spectrogram TransformerCode2
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
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion RecognitionCode1
Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion RecognitionCode1
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
LSSED: a large-scale dataset and benchmark for speech emotion recognitionCode1
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
Deep Residual Local Feature Learning for Speech Emotion Recognition—0
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
Efficient Arabic emotion recognition using deep neural networksCode0
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition—0
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetCode1
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation LearningCode1
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain 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