An Initial Analysis of the Hospital Soundscape via the Language Model Based on the BERT (Bidirectional Encoder Representation from Transformers)

Hospitals are one of the significant public spaces in which healthcare services are provided. Therefore, hospitals should be designed to enhance the relaxation and well-being of patients, their relatives, and healthcare workers. To guide the design of a comfortable hospital environment, the user's spatial perception must be correctly understood, and the emotional and perceptual clusters must be accurately captured. This study aims to start the preparation of a large dataset based on how users describe their hospital space/experiences/perceptions in their natural languages. Developed by Google Language AI, the BERTopic model based on the BERT (Bidirectional Encoder Representations from Transformers) language representation model has been used to analyze text data in the literature and open-source datasets about the hospital soundscape. To establish a system where these data are combined with the visual data of the space using BERTopic and other state-of-the-art models, ensuring the audio-visual integrity and relationships are present.