Andrzej Czyzewski – andczyz@gmail.com
Gdańsk University of Technology
Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department
Gdańsk, Pomerania 80-233
Poland
Popular version of 1aSP6 – Strategies for Preprocessing Speech to Enhance Neural Model Efficiency in Speech-to-Text Applications
Presented at the 187th ASA Meeting
Read the abstract at https://eppro01.ativ.me/appinfo.php?page=IntHtml&project=ASAFALL24&id=3771522&server=eppro01.ativ.me
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Effective communication in healthcare is essential, as accurate information can directly impact patient care. This paper discusses research aimed at improving speech recognition technology to help medical professionals document patient information more effectively. By using advanced techniques, we can make speech-to-text systems more reliable for healthcare, ensuring they accurately capture spoken information.
In healthcare settings, professionals often need to quickly and accurately record patient interactions. Traditional typing can be slow and error-prone, while speech recognition allows doctors to dictate notes directly into electronic health records (EHRs), saving time and reducing miscommunication.
The main goal of our research was to test various ways of enhancing speech-to-text accuracy in healthcare. We compared several methods to help the system understand spoken language more clearly. These methods included different ways of analyzing sound, like looking at specific sound patterns or filtering background noise.
In this study, we recorded around 80,000 voice samples from medical professionals. These samples were then processed to highlight important speech patterns, making it easier for the system to learn and recognize medical terms. We used a method called Principal Component Analysis (PCA) to keep the data simple while ensuring essential information was retained.
Our findings showed that combining several techniques to capture speech patterns improved system performance. We saw an average accuracy improvement, with fewer word and character recognition errors.
The potential benefits of this work are significant:
- Smoother documentation: Medical staff can record notes more efficiently, freeing up time for patient care.
- Improved accuracy: Patient records become more reliable, reducing the chance of miscommunication.
- Better healthcare outcomes: Enhanced communication can improve the quality of care.
This study highlights the promise of advanced speech recognition in healthcare. With further development, these systems can support medical professionals in delivering better patient care through efficient and accurate documentation.
Figure1. Frontpage of the ADMEDVOICE corpus containing medical text and their spoken equivalents