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 151–200 of 431 papers

TitleStatusHype
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP—0
Describing emotions with acoustic property prompts for speech emotion recognition—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
Deep scattering network for speech emotion recognition—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
A cross-corpus study on speech emotion recognition—0
Deep Residual Local Feature Learning for Speech Emotion Recognition—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms—0
Analysis of constant-Q filterbank based representations for speech emotion recognition—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech—0
A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions—0
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition—0
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition—0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning—0
Cross Lingual Cross Corpus Speech Emotion Recognition—0
Cross-lingual and Multilingual Speech Emotion Recognition on English and French—0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
A Survey on Speech Large Language Models—0
AHD ConvNet for Speech Emotion Classification—0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition—0
CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
A study on cross-corpus speech emotion recognition and data augmentation—0
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition—0
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition—0
Conditioning LLMs with Emotion in Neural Machine Translation—0
Forewords—0
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate—0
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition—0
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition—0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition—0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?—0
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—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