Deep Learning-Based Compliance Assessment for Chinese Rail Transit Dispatch Speech

Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms of accuracy and robustness. Building upon the baseline Whisper model, two key enhancements are introduced: (1) low-rank adaptation (LoRA) fine-tuning to better adapt the model to the specific acoustic and linguistic characteristics of rail transit dispatch speech, and (2) a novel entity-aware attention mechanism that incorporates named entity recognition (NER) embeddings into the decoder. This mechanism enables attention computation between words belonging to the same entity category across different commands and recitations, which helps highlight keywords critical for compliance assessment and achieve precise inter-sentence element alignment. Experimental results on real-world test sets demonstrate that the proposed model improves recognition accuracy by 30.5% compared to the baseline model. In terms of robustness, we evaluate the relative performance retention under severe noise conditions. While Zero-shot, Full Fine-tuning, and LoRA-only models achieve robustness scores of 72.2%, 72.4%, and 72.1%, respectively, and the NER-only variant reaches 88.1%, our proposed approach further improves to 89.6%. These results validate the model’s significant robustness and its potential to provide efficient and reliable technical support for ensuring the normative use of dispatch speech in urban rail transit operations.