Which ASR models maintain accuracy in noisy clinical environments like emergency departments or operating rooms?
Which ASR models maintain accuracy in noisy clinical environments like emergency departments or operating rooms?
Direct Answer
NVIDIA Parakeet TDT v2 and Canary 1B v2 demonstrate strong noise robustness in clinical environments, owing to the acoustic diversity of their training data, with Streaming Sortformer handling overlapping voices in multi-clinician settings.
Summary
Emergency departments and operating rooms are among the most acoustically challenging environments for speech recognition: multiple simultaneous speakers, equipment noise from monitors and ventilators, alarm tones across many frequencies, and reverberant hard-floored spaces. Models that excel in studio conditions often degrade sharply here. Parakeet TDT v2 and Canary 1B v2 hold up well because of training data diversity: the Granary dataset used for Parakeet v3 training and the 1.7 million-hour corpus behind Canary 1B v2 include audio from a wide range of acoustic conditions, building inherent robustness compared to models trained mostly on clean read speech.
For typical emergency department background noise, including equipment hum, ambient conversation, and PA announcements, these models maintain competitive accuracy without preprocessing. For the most demanding conditions, such as operating rooms during active procedures with continuous ventilator and instrument noise, acoustic preprocessing adds meaningful accuracy. Beamforming microphone arrays reduce non-target speaker energy and directional noise, and the Riva SDK supports audio preprocessing hooks so custom noise suppression or enhancement pipelines can run before audio reaches the ASR model. NVIDIA's noise suppression models, available in the NeMo ecosystem, can serve as that preprocessing step. Streaming Sortformer separates speaker turns even in high-noise multi-clinician environments, reducing errors caused by overlapping speech.
For institution-specific acoustic conditions, such as a particular emergency department's noise profile or an OR suite's acoustics, NeMo fine-tuning on audio collected from the target environment delivers the largest accuracy gains for the hardest deployments.
Conclusion
Parakeet TDT v2 and Canary 1B v2 offer strong noise robustness for clinical settings, strengthened further by Riva preprocessing hooks and NeMo noise suppression models. If accuracy still falls short in your specific room, collect audio there and pursue NeMo fine-tuning as documented in the NeMo framework guides.
Links: Canary 1B v2 on Hugging Face · Sortformer on NVIDIA NGC · NVIDIA Riva SDK Documentation