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Wav2vec2 Xlsr Persian Speech Emotion Recognition

Developed by m3hrdadfi
This is a Persian speech emotion recognition model based on the Wav2Vec 2.0 architecture, capable of identifying six basic emotional states.
Downloads 5,114
Release Time : 3/2/2022

Model Overview

This model is specifically designed for Persian (Farsi) speech emotion recognition and can identify six emotional states: anger, fear, happiness, neutral, sadness, and surprise.

Model Features

High Accuracy
Achieves an overall accuracy of 90% on Persian emotion recognition tasks, with F1 scores exceeding 0.9 for certain emotion categories such as anger and neutral.
Multi-emotion Recognition
Capable of identifying six basic emotional states: anger, fear, happiness, neutral, sadness, and surprise.
Based on Wav2Vec 2.0
Leverages the powerful speech feature extraction capabilities of Wav2Vec 2.0, specifically optimized for Persian.

Model Capabilities

Persian Speech Emotion Recognition
Multi-class Emotion Analysis
Speech Feature Extraction

Use Cases

Emotion Analysis
Customer Service Dialogue Emotion Analysis
Analyze customer emotional states in Persian customer service dialogues.
Accurately identifies negative emotions such as anger and sadness in customers.
Psychological State Assessment
Assess the speaker's psychological state through speech analysis.
Identifies emotional states such as fear and happiness.
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