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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 431 papers

TitleStatusHype
Persian Speech Emotion Recognition by Fine-Tuning Transformers—0
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition—0
Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers—0
Pitch-Synchronous Single Frequency Filtering Spectrogram for Speech Emotion Recognition—0
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning—0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition—0
Representation Learning with Graph Neural Networks for Speech Emotion Recognition—0
Research on several key technologies in practical speech emotion recognition—0
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition—0
Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition—0
SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech—0
Self-paced ensemble learning for speech and audio classification—0
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts—0
Semi-supervised cross-lingual speech emotion recognition—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset—0
SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning—0
SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults—0
Shallow over Deep Neural Networks: A empirical analysis for human emotion classification using audio data—0
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations—0
Speaker Attentive Speech Emotion Recognition—0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
Speaker Normalization for Self-supervised Speech Emotion Recognition—0
Speech and Text-Based Emotion Recognizer—0
Speech Emotion Recognition Based on CNN+LSTM Model—0
Speech Emotion Recognition Based on Multi-feature and Multi-lingual Fusion—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
Speech Emotion Recognition Considering Local Dynamic Features—0
Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—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
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Speech Emotion Recognition using Self-Supervised Features—0
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring—0
Speech Emotion Recognition using Support Vector Machine—0
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network—0
Speech Emotion Recognition via Contrastive Loss under Siamese Networks—0
結合非線性動態特徵之語音情緒辨識(Speech Emotion Recognition via Nonlinear Dynamical Features)[In Chinese]—0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations—0
Speech Emotion Recognition with Dual-Sequence LSTM Architecture—0
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation—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