Musalia M, Laha S, Cazalilla-Chica J, Allan J, Roach L, Twamley J, Nanda S, Verlander M, Williams A, Kempe I, Patel II, Campbell-West F, Blackwood B, McAuley DF. A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study.
Crit Care 2023;
27:277. [PMID:
37430313 DOI:
10.1186/s13054-023-04420-x]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/27/2023] [Indexed: 07/12/2023] Open
Abstract
OBJECTIVES
Evaluating effectiveness of speech/phrase recognition software in critically ill patients with speech impairments.
DESIGN
Prospective study.
SETTING
Tertiary hospital critical care unit in the northwest of England.
PARTICIPANTS
14 patients with tracheostomies, 3 female and 11 male.
MAIN OUTCOME MEASURES
Evaluation 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.
RESULTS
A 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%.
CONCLUSION
This 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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