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Speech Emotion Recognition With Facebook Wav2vec2 Large Xlsr 53

Developed by firdhokk
A speech emotion recognition system fine-tuned on Wav2Vec2 Large XLSR-53 model, capable of identifying 7 common emotions
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Release Time : 9/20/2024

Model Overview

This model achieves speech emotion classification by fine-tuning Wav2Vec2 Large XLSR-53, supporting recognition of 7 emotions: anger, disgust, fear, happiness, neutral, sadness, and surprise

Model Features

High-Accuracy Emotion Recognition
Achieves 91.68% accuracy and 91.66% F1-score on test sets
Multi-Dataset Training
Trained on combined datasets including RAVDESS, SAVEE, TESS, and URDU
Efficient Feature Extraction
Uses Wav2Vec2 feature processor for audio data, enabling standardized feature input

Model Capabilities

Speech Emotion Recognition
Audio Classification
Multi-Emotion Classification

Use Cases

Human-Computer Interaction
Smart Customer Service Emotion Analysis
Analyzes emotional states in customer voice
Improves service response quality and user experience
Mental Health
Emotional State Monitoring
Tracks user's emotional changes through voice analysis
Assists mental health assessment
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