Evidence map›Paper›PMID 38991167›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2024

How People Living With Amyotrophic Lateral Sclerosis Use Personalized Automatic Speech Recognition Technology to Support Communication.

Richard Cave

Abstract read
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Article
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  8. Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum.Journal of speech, language, and hearing research : JSLHR · 2024
    Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Richard CaveUniversity College London, United Kingdom.ORCID 0000-0002-6410-8200

Funding

Research Symposium in Communication Sciences and DisordersR13DC003383 · NIDCD · AMERICAN SPEECH-LANGUAGE-HEARING ASSN · PI MARGARET A. ROGERS · 1997 to 2026
$727k
NIDCD NIH HHS R13 DC003383
6 · The paper itself

Abstract

purposeAmyotrophic lateral sclerosis (ALS) is a progressive, ultimately fatal disease causing progressive muscular weakness. Most people living with ALS (plwALS) experience dysarthria, eventually becoming unable to communicate using natural speech. Many wish to use speech for as long as possible. Personalized automated speech recognition (ASR) model technology, such as Google's Project Relate, is argued to better recognize speech with dysarthria, supporting maintenance of understanding through real-time captioning. The objectives of this study are how plwALS and communication partners use Relate in everyday conversation over a period of up to 12 months and how it may change with any decline in speech over time.

methodThis study videoed interactions between three plwALS and communication partners. We assessed ASR caption accuracy and how well they preserved meaning. Conversation analysis was used to identify participants' own organizational practices in the accomplishment of interaction. Thematic analysis was used to understand better the participants' experiences of using ASR captions.

resultsAll plwALS reported lower-than-expected ASR accuracy when used in conversation and felt ASR captioning was only useful in certain contexts. All participants liked the concept of live captioning and were hopeful that future improvements to ASR accuracy may support their communication in everyday life.

conclusionsTraining is needed on best practices for customization and practical use of ASR technology and for the limitations of ASR in conversational settings. Support is needed for those less confident with technology and to reduce misplaced allocation of ownership of captioning errors, risking negative effects on psychological well-being.

Indexed as

Amyotrophic Lateral SclerosisDysarthriaSpeech Recognition SoftwareAgedCommunicationCommunication Devices for People with DisabilitiesFemaleHumansMaleMiddle Aged

Identifiers

PMID38991167
PMCPMC12379579

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.