Symphony for Speech-to-Text: Supporting Real-Time Medical Voice Interfaces

2026-05-15

AI Summary

This work addresses the challenges of medical speech recognition, including domain-specific terminology, contextual ambiguity, and accurate transcription of clinical abbreviations and numerical values—issues that existing systems struggle to reconcile with real-time performance, accuracy, and generalization. The authors propose a modular decoupled architecture that separates the transcription pipeline into three specialized stages: domain-adapted recognition, formatting, and context-aware correction. This approach achieves, for the first time, high-recall recognition of medical terms and generates structured clinical text while supporting adaptive deployment across diverse scenarios. The system offers a production-grade API compatible with real-time dictation, conversational input, and batch processing. Evaluated on public medical speech datasets, it significantly outperforms state-of-the-art methods in the clinical domain while matching or exceeding their performance on general-domain tasks. The study also introduces the first Chinese clinical speech benchmark dataset to advance research in this area.

Abstract

After decades of use in dictation and, more recently, ambient documentation, speech is emerging as a primary modality for interacting with technology and AI in healthcare. Yet medical speech recognition remains difficult: systems must capture specialized terminology, resolve contextual ambiguity, and render measurements, abbreviations, and clinical shorthand precisely. Existing solutions are typically optimized either for general-purpose transcription or narrow dictation workflows, limiting their reliability in safety-critical settings and their usefulness for broader clinical workflows. We introduce Symphony for Speech-to-Text, a medical-grade speech recognition system for real-time streaming and batch file-based clinical use. Symphony decomposes the transcription process into specialized components for recognition, formatting, and contextual correction to optimize medical term recall while producing clinically structured text in real time and adapting across use cases. Evaluations on public benchmark and medical speech datasets show that Symphony substantially outperforms state-of-the-art systems in clinical settings while matching or exceeding them in general-domain settings, suggesting robust generalization rather than overfitting. We release a clinical benchmark dataset to support reliable validation and further progress in medical speech recognition. Symphony is available through a production-grade API for live dictation, conversational transcription, and batch audio file processing.

Problem

• medical speech recognition
• clinical terminology
• contextual ambiguity
• real-time transcription
• clinical workflows

Innovation

• medical speech recognition
• real-time transcription
• contextual correction
• structured clinical text
• modular ASR architecture