Corti Symphony for Speech-to-Text is a clinical-specific speech recognition service released by medical AI company Corti on May 20, 2026. It aims to provide real-time transcription for medical professionals, transcription of clinical conversations, and batch processing of recorded files within a single API framework. While general Speech-to-Text services are like universal scribes that transcribe everyday language, Symphony is closer to a clinical specialist assistant trained to understand drug names, diagnoses, dosages, and medical abbreviations. It goes beyond simply converting speech to text and supports structured output that can be processed in clinical workflows, with the key feature being the provision of medical speech models that handle multiple languages. General speech recognition systems may show high accuracy in typical conversations, but in clinical settings where drug names with similar pronunciations, dosages including units, and specialized diagnoses and abbreviations appear consecutively, errors can accumulate. These errors are not just simple typos but lead to increased time spent reviewing medical records and decreased data quality. Symphony's differentiation lies in its design of the model and output system, focusing on medical vocabulary and speech formatting commands. Corti reported that in its own medical speech evaluation, the Word Error Rate (WER) was up to 93% lower compared to general models. However, this figure is the result of an evaluation conducted by Corti, so independent validation reflecting the target language, medical specialty, recording equipment, and noise conditions is necessary before actual implementation. Clinical researchers can connect Symphony as a transcription layer for in-office conversations or remote consultation recordings, and the returned structured results can be passed to a pre-input review screen for electronic medical records or a natural language processing pipeline. For example, after batch processing a 30-minute outpatient consultation recording, extracting drug names, diagnoses, and dosage expressions, and comparing them with the original audio, a review-centric workflow can be established instead of manual transcription. In real-time transcription, medical professionals can use speech formatting commands to create records, and a method can be applied to confirm patient identification information and clinical key items before saving the results. In pharmaceutical and clinical trial environments, multi-site interviews or adverse event consultation audio can be processed with the same transcription API, and the structured text can be used as input for case report form review or safety signal exploration. The support for multilingual models provides the possibility of connecting medical audio from different language regions to a common processing flow, but specific supported languages and regional accuracy require verification in the official documentation. Furthermore, medical audio contains sensitive personal information, so in actual operation, the data storage location, retention period, encryption, access control, and compliance with relevant medical information regulations must be verified separately in the contract and security documents.

Corti Symphony for Speech-to-Text is a clinical-specific speech recognition service released by medical AI company Corti on May 20, 2026. It aims to provide real-time transcription for medical professionals, transcription of clinical conversations, and batch processing of recorded files within a single API framework. While general Speech-to-Text services are like general scribes that transcribe everyday language, Symphony is more like a clinically trained assistant that understands drug names, diagnoses, dosages, and medical abbreviations. It goes beyond simply converting speech to text and enables subsequent processing within the clinical workflow.

🔬 Outpatient Clinic Conversation Transcription: For example, process a 30-minute clinical recording in batch mode, extract drug names, diagnoses, and dosage expressions from the structured results, and compare them with the original audio. Re-validate the developer's claim of up to 93% lower WER compared to general models using a real-world sample from each clinical department to assess the efficiency of record review.