Person Reference as a Trouble Source in Dysarthric Talk-in-Interaction

Bloch, Steven; Saldert, Charlotta

doi:10.1007/978-3-030-28799-3_12

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…The listener's familiarity with the dysarthric speaker's language choices across a set of situational markers would typically help or hinder communication between a dysarthric speaker and listener [24], [25]. Otherwise, the listener would have to search a dimensionally larger prediction space in order to understand some dysarthric speech.…”

Section: Related Workmentioning

confidence: 99%

Ontological Concepts and Logical Listeners for Dysarthric Speech Understanding

Alaka,

Shibwabo Kasamani

2024

IJCDS

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Depending on the social features of the speaker and the social setting in which they are speaking, the relationship between meaning and context might alter. In order to interpret the meaning from dysarthric speech, this paper proposes a theoretical framework for employing speech-event representations, also known as situational projections. The multi-layered approach has been broken down into four main components: a few-shot learner that builds up speaker familiarity; a situational projection component that marshals natural sentences and the built-up familiarity markers into a vector triple; a contextualizer that builds up ontological concepts of the input triple; and finally, a transducer that assumes the function of a logical listener.

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Section: Related Workmentioning

confidence: 99%

Ontological Concepts and Logical Listeners for Dysarthric Speech Understanding

Alaka,

Shibwabo Kasamani

2024

IJCDS

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“…Having knowledge of the topic of discussion during discourse may be helpful in understanding what the patient with dysarthria is saying; however, it has been shown that even the patterns of intertopic switching by these speakers are outside of the usual discourse norms. As such, the primary task of listener-targeted remediation in offsetting the intelligibility burden associated with dysarthria from the speaker is left with the listener [3,4].…”

Section: Introductionmentioning

confidence: 99%

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

Alaka¹,

Kasamani²

2023

JMIR Rehabil Assist Technol

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Background Speech intelligibility and speech comprehension for dysarthric speech has attracted much attention recently. Dysarthria is characterized by irregularities in the speed, strength, pitch, breath control, range, steadiness, and accuracy of muscle movements required for articulatory aspects of speech production. Objective This study examined the contributions made by other studies involved in dysarthric speech comprehension. We focused on the modes of meaning extraction used in generalizing speaker-listener underpinnings in light of semantic ontology extraction as a desired technique, applied method types, speech representations used, and databases sourced from. Methods This study involved a systematic literature review using 7 electronic databases: Cochrane Database of Systematic Reviews, Web of Science Core Collection, Scopus, PubMed, ACM, IEEE Xplore, and Google Scholar. The main eligibility criterion was the extraction of meaning from dysarthric speech using natural language processing or understanding approaches to improve on dysarthric speech comprehension. In total, out of 834 search results, 30 studies that matched the eligibility requirements were acquired following screening by 2 independent reviewers, with a lack of consensus being resolved through joint discussion or consultation with a third party. In order to evaluate the studies’ methodological quality, the risk of bias assessment was based on the Cochrane risk-of-bias tool version 2 (RoB2) with 23 of the studies (77%) registering low risk of bias and 7 studies (33%) raising some concern over the risk of bias. The overall quality assessment of the study was done using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis). Results Following a review of 30 primary studies, this study revealed that the reviewed studies focused on natural language understanding or clinical approaches, with an increase in proposed solutions from 2020 onwards. Most studies relied on speaker-dependent speech features, while others used speech patterns, semantic knowledge, or hybrid approaches. The prevalent use of vector representation aligned with natural language understanding models, while Mel-frequency cepstral coefficient representation and no representation approaches were applied in neural networks. Hybrid representation studies aimed to reconstruct dysarthric speech or improve comprehension. Comprehensive databases, like TORGO and UA-Speech, were commonly used in combination with other curated databases, while primary data was preferred for specific or unique research objectives. Conclusions We found significant gaps in dysarthric speech comprehension characterized by the lack of inclusion of important listener or speech-independent features in the speech representations, mode of extraction, and data sources used. Further research is therefore proposed regarding the formulation of models that accommodate listener and speech-independent features through semantic ontologies that will be useful in the inclusion of key features of listener and speech-independent features for meaning extraction of dysarthric speech.

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

Cave

2024

J Speech Lang Hear Res

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Purpose: Amyotrophic 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. Method: This 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. Results: All 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. Conclusions: Training 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.

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Person Reference as a Trouble Source in Dysarthric Talk-in-Interaction

Cited by 6 publications

References 29 publications

Ontological Concepts and Logical Listeners for Dysarthric Speech Understanding

Ontological Concepts and Logical Listeners for Dysarthric Speech Understanding

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review

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

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