| Property | Value |
|---|---|
| Parameter Count | 319M |
| License | MIT |
| Tensor Type | F32 |
| Paper | View Paper |
What is SER-Odyssey-Baseline-WavLM-Multi-Attributes?
This is a state-of-the-art Speech Emotion Recognition (SER) model developed for the Odyssey 2024 Emotion Recognition competition. Built on the WavLM architecture, it specializes in multi-attribute prediction, analyzing speech to determine three key emotional dimensions: arousal, dominance, and valence, with outputs ranging from 0 to 1.
Implementation Details
The model leverages the MSP-Podcast dataset and implements a multi-task learning approach. It demonstrates impressive performance with Concordance Correlation Coefficient (CCC) scores ranging from 0.405 to 0.688 across different emotional attributes.
- Trained on the comprehensive MSP-Podcast dataset
- Uses PyTorch framework with Transformers architecture
- Implements audio classification pipeline
- Supports F32 tensor operations
Core Capabilities
- Multi-attribute emotion prediction (arousal, dominance, valence)
- High performance on both Test3 and Development sets
- Real-time audio processing capability
- Robust speech emotion recognition across varying conditions
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its multi-attribute prediction capability and its role as a baseline model for the Odyssey 2024 competition. It achieves impressive CCC scores, particularly in dominance and valence prediction.
Q: What are the recommended use cases?
The model is ideal for speech emotion analysis in research, human-computer interaction, and applications requiring detailed emotional state analysis from speech. It's particularly suitable for scenarios requiring continuous values rather than discrete emotion categories.