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Ser Model Adjusted 2023 03 03
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Ser Model Adjusted 2023 03 03

Developed by aherzberg
A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 75.73% on the evaluation set
Audio Classification
Transformers
Open Source License:Apache-2.0#Speech Emotion Recognition#wav2vec2 fine-tuning#High Accuracy
Downloads 18
Release Time : 3/3/2023

Model Overview

This model is a speech emotion recognition model based on the wav2vec2 architecture, used to identify emotional states from speech.

Model Features

High Accuracy
Achieves 75.73% accuracy on the evaluation set
Based on wav2vec2 Architecture
Utilizes the powerful speech feature extraction capability of wav2vec2
End-to-End Training
Learns emotional features directly from raw speech signals

Model Capabilities

Speech Emotion Recognition
Speech Feature Extraction
Emotion Classification

Use Cases

Human-Computer Interaction
Intelligent Customer Service Emotion Analysis
Analyzes emotional states in customer speech
Improves customer service quality
Mental Health
Emotional State Monitoring
Analyzes user emotional changes through speech
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