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Medical ASR - Sahal AI

Speech & Healthcare AI

FUNDING

$425K

Seed Round Secured

EXPERIMENTS

200+

Training Runs

PRODUCTIVITY

2x

Annotation Speed

WER

8.3%

Word Error Rate

DATASET

1,500+

Hours of Medical Audio

Background

Where dialect barriers disrupt the clinical workflow

Documentation becomes a bottleneck when technology cannot keep pace with the natural spoken word. Every moment spent correcting a transcript is a moment stolen from a patient's bedside.

The Intellema Design Challenge

Healthcare professionals in Saudi Arabia face significant documentation burdens due to the limitations of standard speech recognition in medical settings. Existing systems often struggle to accurately capture specific regional dialects and complex clinical terminology during patient consultations.

The project required a custom ASR system tailored for Saudi Arabic dialects to convert spoken interactions into structured clinical notes. It focused on enhancing model robustness through synthetic data and audio cleaning to ensure reliable performance in high-stakes environments.

  • Dialectal Complexity
  • Documentation Burden
  • Medical Accuracy
  • Data Constraints

Our Approach

Tech Stack

PyTorch
Hugging Face
Docker
AWS
FastAPI

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