wav2vec-english-speech-emotion-recognition
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch |
| Accuracy | 97.46% |
| Base Model | wav2vec2-large-xlsr-53-english |
What is wav2vec-english-speech-emotion-recognition?
This model represents a significant advancement in speech emotion recognition (SER), built upon the wav2vec2 architecture. It's specifically fine-tuned to recognize seven distinct emotions: anger, disgust, fear, happiness, neutral, sadness, and surprise. The model leverages three prominent emotional speech datasets (SAVEE, RAVDESS, and TESS), providing a robust foundation for emotion detection in spoken English.
Implementation Details
The model is trained using carefully selected hyperparameters, including a learning rate of 0.0001 and Adam optimizer with betas=(0.9,0.999). Training was conducted over 4 epochs with a maximum of 7,500 steps, using gradient accumulation and a batch size of 4. The training progression showed remarkable improvement, starting from 48.6% accuracy and reaching 97.46% in the final evaluation.
- Comprehensive training on 4,720 audio files from multiple speakers
- Balanced gender representation in training data
- Gradient accumulation steps: 2
- Save checkpoints every 1,500 steps
Core Capabilities
- High-accuracy emotion classification (97.46%)
- Support for 7 distinct emotional states
- Real-time speech emotion analysis
- Cross-gender emotional recognition
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its exceptional accuracy (97.46%) and its comprehensive training on diverse datasets including both male and female voices, making it particularly robust for real-world applications. The use of wav2vec2 as a base architecture provides strong speech recognition capabilities that are then specialized for emotion detection.
Q: What are the recommended use cases?
This model is ideal for applications in customer service analysis, mental health monitoring, automated call center emotion tracking, and research in human-computer interaction. It's particularly suited for English-language applications requiring nuanced emotion detection.
