Hubert Large Speech Emotion Recognition Russian Dusha Finetuned
This model is a Russian speech emotion recognition model fine-tuned on the HuBERT architecture, trained on the DUSHA dataset, capable of identifying emotional states such as neutral, anger, positivity, and sadness.
Downloads 111.13k
Release Time : 5/28/2023
Model Overview
This is a deep learning model specifically designed for Russian speech emotion recognition, fine-tuned from the facebook/hubert-large-ls960-ft pre-trained model, suitable for speech emotion analysis applications.
Model Features
High-Accuracy Emotion Recognition
Achieves 86% accuracy and 81% F1 score on the test set, outperforming the baseline.
Optimized for Russian
Specially fine-tuned using the Russian DUSHA dataset, ideal for Russian speech emotion analysis.
Efficient Fine-Tuning Strategy
Employs partial layer freezing and semi-dataset training to enhance training efficiency while maintaining performance.
Model Capabilities
Russian Speech Emotion Classification
Audio Feature Extraction
Emotion State Recognition
Use Cases
Emotion Analysis
Customer Service Voice Emotion Monitoring
Analyzes customer emotional changes during service calls.
Can identify negative emotions like anger for timely alerts.
Mental Health Assessment
Evaluates emotional states of depression patients through voice analysis.
Can detect trends in sadness-related emotional changes.
Human-Computer Interaction
Intelligent Voice Assistant
Adjusts response strategies based on user voice emotions.
Provides a more human-like interaction experience.
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