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cloud.google.com·6h ago
AI Research
arXiv CS.AI·24d ago·5 min read
Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition
TL;DR
A new method for emotion recognition in speech using SpeechLLMs improves accuracy by adapting the model to classify emotions directly from hidden states. This approach enhances performance on realistic transcripts while reducing errors from incorrect label generation.
✦ Why It Matters
Implement this discriminative adaptation method to enhance your emotion recognition systems today.
Key Takeaways
- Utilize hidden states for direct emotion classification.
- Implement a linear layer for improved interpretability.
- Focus on realistic ASR transcripts for better performance.
- Explore biases in language models for emotion mapping.
How It Works
The proposed method leverages the hidden state of the final prompt token in SpeechLLMs, applying a classification head to produce emotion labels directly. This approach contrasts with traditional generative methods, which can generate irrelevant outputs.
By maintaining a single linear layer, the model achieves a balance between accuracy and interpretability, allowing each emotion to correspond to a specific direction in the token space.