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.
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.
| Original language | English |
|---|---|
| Article number | 277 |
| Number of pages | 6 |
| Journal | Critical Care |
| Volume | 27 |
| Publication status | Published - 10 Jul 2023 |