Ser Model Fixed Label
S
Ser Model Fixed Label
Developed by aherzberg
A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 83.67% on the evaluation set
Downloads 18
Release Time : 2/28/2023
Model Overview
This model is a speech emotion recognition model based on the wav2vec2 architecture, used to identify emotion categories from speech
Model Features
High Accuracy
Achieves 83.67% accuracy on the evaluation set
Based on wav2vec2 Architecture
Uses facebook's wav2vec2-base as the base model
End-to-End Training
Directly processes raw audio input without complex feature engineering
Model Capabilities
Speech Emotion Recognition
Audio Classification
Use Cases
Sentiment Analysis
Customer Service Call Analysis
Analyze customer emotional states in service calls
Can recognize 83.67% of emotion categories
Mental Health Assessment
Assess user emotional states through speech analysis
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