Speech Emotion Recognition
16개 벤치마크 · 논문 516편 · 이 태스크의 논문 보기 →
Benchmarks
CREMA-D
IEMOCAP
RAVDESS
MSP-Podcast (Activation)
MSP-Podcast (Dominance)
MSP-Podcast (Valence)
BERSt
RESD
Dusha Crowd
Dusha Podcast
EMODB
EmoDB Dataset
LSSED
MSP-IMPROV
Quechua-SER
ShEMO
Most implemented
Attention Is All You Need
Continuous control with deep reinforcement learning
AST: Audio Spectrogram Transformer
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Multimodal Speech Emotion Recognition Using Audio and Text
Speech Emotion Diarization: Which Emotion Appears When?
Papers
Dual-Scale State-Space Modeling with Speaker-Wise Dynamic CRF for Speech Emotion Recognition in Conversation
Conversational speech emotion recognition must reconcile acoustic evidence across temporal scales with two interaction processes: cross-speaker contextual influence and within-speaker emotion evolution. We propose DSSM-C…
Speech Emotion RecognitionAMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition
On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher knowledge distillation can compress the…
Speech Emotion RecognitionKnowledge DistillationLearning from Annotation Uncertainty: Entropy-Aware Curriculum for Speech Emotion Recognition
Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement. We study distribution-based supervision for 9-class SER on MSP-Podcast 2.0 using a WavLM-Base multitask model f…
Speech Emotion RecognitionSpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models
As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural human-AI communication. However, existing …
Speech Emotion RecognitionEmotional IntelligenceScaling Audio Models Efficiently: A Joint Study of Compute Constraints and Optimization Behavior
In this paper, we investigate the tradeoffs between compute allocation and model performance for two speech processing tasks: Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER). We propose a unified …
Speech Emotion RecognitionSpeech RecognitionEmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
Reviewing recorded interviews for affective cues such as composure, hesitation and agitation is slow and subjective, and cloud services that could automate it require sensitive audio to leave the device. EmotionAI is a f…
Speech Emotion RecognitionSpeech RecognitionQuestion Answering