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Xlsr Wav2vec Speech Emotion Recognition

Developed by harshit345
A speech emotion recognition model based on the XLSR-Wav2Vec architecture, capable of identifying five basic emotions: anger, disgust, fear, happiness, and sadness.
Downloads 498
Release Time : 3/2/2022

Model Overview

This model uses the Wav2Vec2 architecture for speech emotion classification, suitable for identifying the emotional state of speakers from speech signals.

Model Features

Multi-emotion Recognition
Capable of identifying five basic emotions: anger, disgust, fear, happiness, and sadness.
Wav2Vec2-based Architecture
Utilizes the self-supervised learning capabilities of Wav2Vec2, performing well on speech emotion recognition tasks.
High Accuracy
Achieves an overall accuracy of 80.6% on test data, with balanced performance across all emotion categories.

Model Capabilities

Speech Emotion Classification
Speech Signal Processing
Emotion Probability Scoring

Use Cases

Human-Computer Interaction
Customer Service System Emotion Analysis
Analyzes the emotional state in customer speech to help the customer service system make smarter responses.
Accurately identifies negative emotions such as anger and dissatisfaction in customers.
Mental Health
Emotional State Monitoring
Analyzes users' emotional changes through daily speech.
Can be used for auxiliary diagnosis of psychological disorders such as depression.
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