New Delhi, 29th October, 2025: Shunyalabs.ai, a leader in Voice AI infrastructure for enterprises, today announced the launch of Zero STT Med, a breakthrough domain-optimized automatic speech recognition (ASR) system purpose-built for medical and clinical workflows.
Powered by Shunyalabs’ proprietary training technology, Zero STT Med delivers state-of-the-art accuracy, real-time responsiveness, and flexible deployment options—on-premises or in the cloud—making it ideal for hospitals, telemedicine providers, ambient scribe systems, and regulated healthcare environments.
Zero STT Med at a Glance
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Unmatched Accuracy: Achieves a Word Error Rate (WER) of 11.1% and Character Error Rate (CER) of 5.1%, surpassing leading medical ASR competitors.
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Rapid Training Efficiency: Fully converges in just 3 days of training on 2 × A100 GPUs with minimal real clinical audio—dramatically reducing data collection and compute requirements.
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Continuous Updates: Fast training cycles allow frequent model refreshes to include new drugs, procedures, and terminologies.
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Real-Time Performance: Delivers high RTFx capability for instant transcription in clinical environments, including consultations, charting, and dictation.
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Privacy-First Deployment: Operates seamlessly on CPU-only on-premises servers, ensuring full data control and compliance with HIPAA, GDPR, and other healthcare regulations.
Addressing the Complexities of Clinical Speech
Healthcare transcription is among the most demanding ASR challenges—featuring rapid speech, domain-specific jargon, and strict privacy constraints. Clinicians often use acronyms, drug names, and numerical shorthand, while multiple speakers may overlap or interrupt each other. Even minor transcription errors can alter critical meaning.
Zero STT Med is engineered to meet these challenges head-on through:
Domain-Aware Vocabulary and Formatting
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Comprehensive coverage of medical terminology, drug names, clinical procedures, and ICD/LOINC codes.
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Automatic normalization for dosages, numerics, and abbreviations, minimizing manual post-editing.
Advanced Speaker Diarization and Context Tracking
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Differentiates between speakers—clinicians, patients, and caregivers—in real time.
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Maintains contextual accuracy even amid background noise or overlapping speech.
Accent-Robust and Low-Bias Training
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Trained on diverse, multilingual data to ensure consistency across accents, dialects, and speaking styles.
Low-Data, Fast Training Advantage
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Proprietary training enables top-tier performance with limited real audio.
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3-day convergence empowers rapid adaptation to evolving medical vocabularies.
Real-Time-First Architecture
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Delivers identical recognition accuracy across streaming and batch modes—removing the latency trade-offs common in legacy ASR systems.
Privacy and Compliance-Ready
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Fully deployable on standard CPU infrastructure, ensuring total data residency and regulatory compliance for hospitals and enterprises.
Key Use Cases
Zero STT Med unlocks next-generation capabilities across healthcare operations:
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Ambient clinical scribing: Real-time structured note generation reduces clinician screen time.
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Live dictation and charting: Enables voice-based orders and summaries with minimal corrections.
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Telemedicine and virtual consults: Produces live transcripts for documentation and analytics.
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Radiology and procedural transcription: Captures real-time voice input during imaging and surgery.
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Archive digitization: Transcribes historical audio for digital records and NLP analysis.
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Edge and mobile deployment: Enables offline transcription in portable healthcare setups.
By reducing transcription errors, Zero STT Med improves clinical documentation quality, reduces administrative workload, and unlocks the potential for AI-powered analytics in healthcare.
Performance Highlights
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Accuracy: Industry-leading results, exceeding commercial ASR baselines.
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Training Efficiency: Full convergence within 3 days, enabling frequent retraining and domain adaptation.
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Deployment Flexibility: Operates on both GPU and CPU infrastructure, unlike typical cloud-only systems.
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Real-Time Consistency: Maintains identical accuracy in streaming and offline modes
Executive Quotes
Ritu Mehrotra, CEO & Founder, Shunyalabs.ai, said:
“At Shunyalabs, we believe medical transcription must be not just fast but flawlessly accurate — every dosage, diagnosis, and timestamp matters. Zero STT Med embodies that vision. We’ve dramatically reduced the cost and time to train, making high-fidelity ASR accessible to more healthcare systems.”
Sourav Banerjee, CTO, Shunyalabs.ai, added:
“Our goal with Zero STT Med wasn’t incremental improvement — it was to redefine medical speech recognition. We’ve built a system with fewer corrections, lower latency, and complete data privacy, setting a new benchmark for clinical ASR.”
Availability and Early Access
Zero STT Med is now open for preview and pilot evaluation by healthcare and healthtech organizations. Shunyalabs is onboarding early partners for integration and feedback, offering on-prem CPU-only options for high-compliance environments.
Currently available in English, support for Indian and other international languages will be added soon.