Application of Conformer Architecture in Clinical Speech Input and Intelligent Medical Record Generation
TL;DR
This study proposes a multi-stage intelligent documentation framework that integrates a 12-layer Conformer architecture, BERT-BiLSTM-CRF semantic modeling, and BART-based structured text generation that illustrates the effectiveness of deep time-frequency feature extraction and contextual sequence modeling for complex noisy signals.
Abstract
Accurate clinical speech recognition remains challenging because rapid pronunciation, domain-specific terminology, and background noise often degrade automatic speech recognition and subsequent medical record generation. This study proposes a multi-stage intelligent documentation framework that integrates a 12-layer Conformer architecture, BERT-BiLSTM-CRF semantic modeling, and BART-based structured text generation. The Conformer encoder captures both local acoustic characteristics and long-range contextual dependencies, while the semantic module performs medical entity recognition and normalization to enhance terminology consistency. The extracted information is subsequently incorporated into a BART generator with clinical knowledge prompts to produce standardized SOAP-compliant medical records. Experimental results demonstrate a word error rate of 6.3%, medical term accuracy of 95.8%, low response latency of approximately 940–960 ms, and generation quality approaching physician-written records. Beyond clinical documentation, the proposed framework illustrates the effectiveness of deep time-frequency feature extraction and contextual sequence modeling for complex noisy signals, offering methodological insights for electromagnetic signal interpretation, antenna measurement data processing, and intelligent information extraction in propagation-related applications.