ArticleCritical care (London, England)2023
A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study.
Article in Critical care (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Utility of a Novel Mobile Lip-Reading Application for Patients After Total Laryngectomy.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- LiDAR-Based Safety Envelope Detection with Accelerometer and DTW for Intrusion Localization in Roller Coasters.Micromachines · 2025Article
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Authors and funding
14 authors.
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Abstract
objectivesEvaluating effectiveness of speech/phrase recognition software in critically ill patients with speech impairments.
designProspective study.
settingTertiary hospital critical care unit in the northwest of England.
participants14 patients with tracheostomies, 3 female and 11 male.
main outcome measuresEvaluation of dynamic time warping (DTW) and deep neural networks (DNN) methods in a speech/phrase recognition application. Using speech/phrase recognition app for voice impaired (SRAVI), patients attempted mouthing various supported phrases with recordings evaluated by both DNN and DTW processing methods. Then, a trio of potential recognition phrases was displayed on the screen, ranked from first to third in order of likelihood.
resultsA total of 616 patient recordings were taken with 516 phrase identifiable recordings. The overall results revealed a total recognition accuracy across all three ranks of 86% using the DNN method. The rank 1 recognition accuracy of the DNN method was 75%. The DTW method had a total recognition accuracy of 74%, with a rank 1 accuracy of 48%.
conclusionThis feasibility evaluation of a novel speech/phrase recognition app using SRAVI demonstrated a good correlation between spoken phrases and app recognition. This suggests that speech/phrase recognition technology could be a therapeutic option to bridge the gap in communication in critically ill patients. WHAT IS ALREADY KNOWN ABOUT THIS TOPIC: Communication can be attempted using visual charts, eye gaze boards, alphabet boards, speech/phrase reading, gestures and speaking valves in critically ill patients with speech impairments. WHAT THIS STUDY ADDS: Deep neural networks and dynamic time warping methods can be used to analyse lip movements and identify intended phrases. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE AND POLICY: Our study shows that speech/phrase recognition software has a role to play in bridging the communication gap in speech impairment.
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