Artikel ; Online: Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models.
IEEE journal of biomedical and health informatics
2024 Band 28, Heft 3, Seite(n) 1692–1703
Abstract: Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows ... ...
Abstract | Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information. |
---|---|
Mesh-Begriff(e) | Humans ; Speech ; Language ; Voice |
Sprache | Englisch |
Erscheinungsdatum | 2024-03-06 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ZDB-ID | 2695320-1 |
ISSN | 2168-2208 ; 2168-2194 |
ISSN (online) | 2168-2208 |
ISSN | 2168-2194 |
DOI | 10.1109/JBHI.2023.3345897 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Zusatzmaterialien
Kategorien
Verfügbar in ZB MED Köln/Königswinter
Zs.A 5483: Hefte anzeigen | Standort: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG) |
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.