Engineering a Clinical-Grade Vocal Fatigue Scoring Pipeline from Raw Speech

## Executive Summary Continuous vocal load monitoring requires instantaneous, clinical-grade acoustic processing. Standard MFCC-based feature extractors often fail under diverse recording noise and acoustic environments. ### Architectural Approach We leveraged **ECAPA-TDNN** pre-trained representations fine-tuned with a supervised contrastive loss objective (VHE). The output vector is mapped through a lightweight calibration head to yield normalized fatigue index values (0.00 to 1.00). ```python from auralis_vfs import VocalFatigueScorer scorer = VocalFatigueScorer.from_pretrained('inference-lab/ecapa-vfs') score = scorer.score_audio('audio_sample.wav') print(f'Fatigue Index: {score.index:.2f}') ``` ### Benchmarks & Latency - **Inference Speed**: 42ms per 5-second window on standard CPU. - **Accuracy**: 78% overall fatigue classification accuracy (2.5x baseline improvement). - **F1 Scores**: 0.85 (Normal), 0.78 (Moderate), 0.70 (Severe).