WIP
Research
Emotional-Aware-Reasoning
Audio-augmented emotion-aware language models with cross-speaker generalization
// DESCRIPTION
Investigating how emotional audio cues can improve language model reasoning and empathy.
Research Focus
- Multi-modal emotion recognition (text + audio)
- Emotion-conditioned response generation
- Cross-speaker generalization
Key Finding
Real emotional speech shows 48% better cross-speaker generalization compared to synthetic speech.
Datasets
- RAVDESS: 24 speakers, 7 emotions
- Synthetic: TTS-generated emotional speech
Experiments
- Audio feature extraction (librosa)
- Multi-modal fusion architectures
- Emotion-guided decoding
// HIGHLIGHTS
- 48% improvement finding
- Multi-modal fusion research
- Multiple experiments complete