EXPERIMENTAL DESIGN & IMPLENTATION METHODODIGAL FLOWCHART

SYNTHETIC CORPUS

• 5,000 Sessions

• 300 Unique Speakers

• Dysarthric Speech Simulators

• Controlled Noise/Perttuations

• Pre-training

• 180 Sessions

• 42 Unique Speakers

• 6,800 Utttances

• Inlinics/Classsoms, De-ides 3-8)

• Demographic Indicatr Analysies

• Pre-training

Concerns

Privacy

Segment Utttances

• Energy-based VAD)

• Pitch Variation & Jitter

• Coined Corens

• Poines Masks

Session Duration

• Utturence Couration & Jitter

• MFCCS (13-dim static + deltas)

• Noise Level (SNR)

• Noise Level (SNR)

Demograhics (Age, Gender, Severity)

Numeric Features Normalized

Data Augmentation (Synthetic Synthetic Corpus ONLY): Time iter Pich, Additive Noise

Transformer?

Log-Mel Spectograms

Hybrid ASR (Ours)

Conv Front-end → Bi GRU → Self-Attention → TabNet (Feature Masks)

Transformer Baseline

Transformerren → Bi GRU → Multi-head Attention, Positional Encodings

TabNer Only

Tabular Model (Engineered Features)

3.4 TRAINING & VALIDATION PROTOCOL

1. Pre-training on Synthetic Data (All Models)

2. Fine trining on Real Data (Hybrid ASR, Transms) with Few-shot Mets-leaming

3. 5-fold Cross-Valldation (Synthefor Hyper-parameter Tuning. Final Evaluation on Held-out Real Corpus

PyTorch, Adam Optimizer, Early Stopping

3.5 PERFORMANCE METRICS

Word-Error Rate (WER)

Reat-Time Factor (RTF)

F1-score

Brier Score

Subgroug Gap

Subgroup Gap (WER Difference)

ETHICAL & REPROUBECIIVILY CONSIDERATIONS

• Institutional Ethics, Informed Consent

• Encrypted Data Storage (Max 3 yrs)

• Federeted Learning (Gradirles + Differential Privacy Noise)

• Real datsset Access (Ethiiical Apporoval)

3.3 Model Framework

3.1 Datasets

3.2 Feature Engineering