Today, Copenhagen-based healthcare AI Corti is launching Symphony for Speech-to-Text, a new generation of clinical-grade speech recognition models engineered specifically for real-time dictation, conversational transcription, and batch audio processing — and their accuracy rate is the highest for this specific use case yet recorded.”We are focused on ensuring our AI scribes can be trusted by physicians, medical practitioners and patients…the entire healthcare system,” said Andreas Cleve, co-founder and CEO of Corti, in an exclusive video call interview with VentureBeat. The performance data the company is bringing to the table paints a stark picture of the current state of enterprise AI: when it comes to highly regulated, specialized industries, domain-specific models can beat out the foundation model providers. In a newly published research paper, Corti revealed that its new clinical-grade speech models reduced word error rates (WER) by up to 93% when compared against leading generalist speech models and APIs on medical terminology. On English medical terminology, its Symphony for Speech-to-Text achieved a remarkably low 1.4% WER. By comparison, OpenAI’s speech model registered a 17.7% WER, ElevenLabs hit 18.1%, Whisper recorded 17.4%, and Parakeet scored 18.9%.Corti’s announcement serves as a critical inflection point for healthcare builders. While general-purpose APIs like OpenAI’s whisper are sufficient for broad-domain transcription, they frequently stumble over medical acronyms, complex medication dosages, shorthand, and noisy emergency room environments. Symphony for Speech-to-Text aims to solve this by providing developers with a highly specialized, production-grade API designed from the ground up for clinical workflows.The agentic era demands flawless data inputsThe launch of Symphony for Speech-to-Text highlights a fundamental shift in how healthcare uses voice technology. For dec …
