A practical implementation of phoneme classification achieving 74% accuracy on real-world speech data. The system processes raw audio into mel-spectrograms, uses context-aware frame analysis, and employs a deep neural network architecture.
- Phonemes span multiple time segments
- Natural speech has uneven phoneme distribution
- Speaker variations and background noise
- Large-scale data processing requirements
Raw audio processing involves:
- 25ms frames with 10ms stride
- Fourier Transform conversion
- Mel-scale filtering for 40 features
- Context window of 85 frames (k=42)
- Input shape:
torch.Size([3400])
- Training: 14,542 utterances (18.4M frames)
- Validation: 2,200 utterances (1.5M frames)
- Testing: 2,200 utterances (1.6M frames)
Total dataset: ~251.77GB
Batch size: 1024 frames (~93MB)Optimized DataLoader implementation includes:
- Multi-threaded loading
- GPU memory pinning
- Selective data shuffling
Layer Structure:
Input (3400) → 1024 → 1024 → 512 → 256 → 128 → 64 → Output (71)Each layer contains:
- Linear transformation
- BatchNorm
- ReLU activation
Test Accuracy: 74.09%
Validation Accuracy: 73.70%
Training Accuracy: 42.53%Key Findings include:
- Overall recognition rate of ~74% for identifying 71 distinct speech sounds
- Misclassifications occur mostly between similar-sounding phonemes
- Clear distinctions maintained between very different sounds
- Reduce dropout rates
- Adjust batch normalization momentum
- Test alternative learning rate schedules
- Deep Learning for AI, Carnegie Mellon University
- PyTorch Documentation
- Deep Learning with PyTorch (Stevens, E., et al., 2020)