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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 201–250 of 431 papers

TitleStatusHype
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
SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning—0
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance—0
Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition—0
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition—0
Support Super-Vector Machines in Automatic Speech Emotion Recognition—0
SyntAct: A Synthesized Database of Basic Emotions—0
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification—0
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models—0
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains—0
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data—0
The Role of Phonetic Units in Speech Emotion Recognition—0
Toward end-to-end interpretable convolutional neural networks for waveform signals—0
Towards adversarial learning of speaker-invariant representation for speech emotion recognition—0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora—0
Towards Machine Unlearning for Paralinguistic Speech Processing—0
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation—0
Transfer Learning for Personality Perception via Speech Emotion Recognition—0
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks—0
TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition—0
Turbo your multi-modal classification with contrastive learning—0
Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions—0
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition—0
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network—0
Unsupervised low-rank representations for speech emotion recognition—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Usefulness of Emotional Prosody in Neural Machine Translation—0
Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots—0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
Versatile audio-visual learning for 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