wav2vec2-xlsr-greek-speech-emotion-recognition
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | m3hrdadfi |
| Dataset | AESDD |
What is wav2vec2-xlsr-greek-speech-emotion-recognition?
This is a specialized speech emotion recognition model designed specifically for the Greek language. Built on the wav2vec2-xlsr architecture, it can accurately classify speech into five distinct emotional states: anger, disgust, fear, happiness, and sadness. The model achieves an impressive overall accuracy of 91%, making it a robust tool for Greek speech emotion analysis.
Implementation Details
The model leverages the wav2vec2 architecture with XLSR (Cross-Lingual Speech Representations) specifically fine-tuned for Greek emotional speech recognition. It processes audio inputs through a feature extractor and provides emotion probability scores as output.
- Supports five emotion classes with high precision (85-96%)
- Requires PyTorch and Transformers libraries for implementation
- Includes built-in audio preprocessing capabilities
- Operates on raw audio input with automatic resampling
Core Capabilities
- Emotion classification with confidence scores
- Exceptional performance in detecting anger (96% F1-score) and sadness (98% F1-score)
- Real-time processing capability
- Robust cross-speaker generalization
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Greek speech, offering state-of-the-art emotion recognition capabilities with particularly high accuracy for anger and sadness detection. Its use of wav2vec2-xlsr architecture ensures robust feature extraction from speech signals.
Q: What are the recommended use cases?
The model is ideal for Greek language applications in sentiment analysis, customer service emotion monitoring, psychiatric applications, and research in Greek speech emotion analysis. It's particularly effective in scenarios requiring high-precision emotion detection in Greek speech.
