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Wav2vec2 Base Toronto Emotional Speech Set

Developed by DunnBC22
An audio emotion classification model fine-tuned based on wav2vec2-base, used to identify the speaker's emotional state.
Downloads 185
Release Time : 5/2/2023

Model Overview

This model is a fine-tuned version of facebook/wav2vec2-base on the Toronto Emotional Speech Set (TESS) dataset, specifically designed for classifying the speaker's emotions in audio samples.

Model Features

High Accuracy Emotion Recognition
Achieves 88.04% accuracy on the evaluation set, effectively identifying multiple emotional states.
Based on wav2vec2 Architecture
Utilizes the powerful wav2vec2-base model as its foundation, with excellent audio feature extraction capabilities.
Comprehensive Evaluation Metrics
Provides multiple evaluation metrics including F1 score, recall, and precision, comprehensively reflecting model performance.

Model Capabilities

Speech Emotion Recognition
Audio Classification
English Speech Analysis

Use Cases

Sentiment Analysis
Customer Service Dialogue Emotion Monitoring
Used to analyze the real-time emotional state of customers in customer service dialogues.
Helps customer service personnel adjust communication strategies promptly.
Psychological State Assessment
Assists psychologists in analyzing patients' speech emotion characteristics.
Provides objective reference indicators for emotional states.
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