Allan, J

Blackwood, Bronagh

Campbell-West, F

Cazalilla-Chica , L

Kempe, I

Lala, S

McAuley, Danny

Musalia, M

Nanda, S

Patel, II

Roach, L

Twamley, J

Verlander, M

Williams, A

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

Musalia, M.

Lala, S.

Blackwood, B.

McAuley, D. F.

Queen's University Belfast Research Portal

A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study

A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study

Abstract

Similar works

Full text

Available Versions

Queen's University Belfast Research Portal