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

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. MA...

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Autores principales: Musalia, M., Laha, S., Cazalilla-Chica, J., Allan, J., Roach, L., Twamley, J., Nanda, S., Verlander, M., Williams, A., Kempe, I., Patel, I. I., Campbell-West, F., Blackwood, B., McAuley, D. F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10332046/
https://www.ncbi.nlm.nih.gov/pubmed/37430313
http://dx.doi.org/10.1186/s13054-023-04420-x
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author Musalia, M.
Laha, S.
Cazalilla-Chica, J.
Allan, J.
Roach, L.
Twamley, J.
Nanda, S.
Verlander, M.
Williams, A.
Kempe, I.
Patel, I. I.
Campbell-West, F.
Blackwood, B.
McAuley, D. F.
author_facet Musalia, M.
Laha, S.
Cazalilla-Chica, J.
Allan, J.
Roach, L.
Twamley, J.
Nanda, S.
Verlander, M.
Williams, A.
Kempe, I.
Patel, I. I.
Campbell-West, F.
Blackwood, B.
McAuley, D. F.
author_sort Musalia, M.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-023-04420-x.
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spelling pubmed-103320462023-07-11 A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study Musalia, M. Laha, S. Cazalilla-Chica, J. Allan, J. Roach, L. Twamley, J. Nanda, S. Verlander, M. Williams, A. Kempe, I. Patel, I. I. Campbell-West, F. Blackwood, B. McAuley, D. F. Crit Care Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-023-04420-x. BioMed Central 2023-07-10 /pmc/articles/PMC10332046/ /pubmed/37430313 http://dx.doi.org/10.1186/s13054-023-04420-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Musalia, M.
Laha, S.
Cazalilla-Chica, J.
Allan, J.
Roach, L.
Twamley, J.
Nanda, S.
Verlander, M.
Williams, A.
Kempe, I.
Patel, I. I.
Campbell-West, F.
Blackwood, B.
McAuley, D. F.
A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title_full A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title_fullStr A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title_full_unstemmed A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title_short A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
title_sort user evaluation of speech/phrase recognition software in critically ill patients: a decide-ai feasibility study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10332046/
https://www.ncbi.nlm.nih.gov/pubmed/37430313
http://dx.doi.org/10.1186/s13054-023-04420-x
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