PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis
This dataset addresses the need for robust speech-controlled systems in medical settings, specifically for surgeons to control operation parameters, but it is incremental as it primarily provides a new resource rather than a novel method.
The authors introduced the PoCaP Corpus, a multimodal dataset of speech, audio, X-ray images, and system commands from 31 interventional radiology procedures, to support the development of a smart speech assistant for surgeons in operating rooms, achieving an initial speech recognition word error rate of 11.52%.
This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-ray images, and system commands collected from 31 PoCaP interventions by six surgeons with average duration of 81.4 $\pm$ 41.0 minutes. The corpus aims to provide a resource for developing a smart speech assistant in operating rooms. In particular, it may be used to develop a speech controlled system that enables surgeons to control the operation parameters such as C-arm movements and table positions. In order to record the dataset, we acquired consent by the institutional review board and workers council in the University Hospital Erlangen and by the patients for data privacy. We describe the recording set-up, data structure, workflow and preprocessing steps, and report the first PoCaP Corpus speech recognition analysis results with 11.52 $\%$ word error rate using pretrained models. The findings suggest that the data has the potential to build a robust command recognition system and will allow the development of a novel intervention support systems using speech and image processing in the medical domain.