Speech Emotion Recognition
Built a real-time speech emotion recognition pipeline combining audio preprocessing, MFCC feature extraction, and hybrid CNN-RNN deep learning models for affective computing.
Related Projects
SYSTEM ARCHITECTURE
Published research focuses on data augmentation and lightweight CNN-based speech emotion recognition.
Problem
Human emotional expression in speech varies significantly across speakers, recording conditions, and languages. The challenge was to build a robust pipeline that generalizes across these variations while maintaining real-time inference performance.
Architecture
Modular pipeline: audio acquisition → preprocessing (normalization, silence removal) → MFCC feature extraction → hybrid CNN-RNN classifier → real-time emotion prediction. Separated signal processing from model inference for maintainability.
Challenges
- Speaker variability and recording quality differences
- Class imbalance across emotion categories
- Background noise robustness
- Variable audio duration handling
- Real-time inference latency constraints
- Generalization across speakers and datasets
Benchmarks
Evaluated on RAVDESS and CREMA-D datasets. Achieved competitive accuracy across 7-8 emotion classes with sub-100ms inference latency for real-time applications.
Lessons Learned
- High-quality preprocessing matters more than model complexity for speech
- MFCCs provide strong perceptual features with low dimensionality
- Hybrid CNN-RNN captures both local patterns and temporal dependencies
- Consistent train/inference pipelines are critical for deployment