Effects of Age-Related Cognitive Decline on Elderly User Interactions with Voice-Based Dialogue Systems

Kobayashi, Masatomo; Kosugi, Akihiro; Takagi, Hironobu; Nemoto, Miyuki; Nemoto, Kiyotaka; Arai, Tetsuaki; Yamada, Yasunori

doi:10.1007/978-3-030-29390-1_4

Cited by 36 publications

(31 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, mobile applications for collecting speech responses to neuropsychological tasks such as verbal fluency, counting backward, and picture description, have been developed and showed accurate classification rates for detecting patients with AD and MCI ( 32 , 33 ). As other examples, vocal characteristics in speech data during typical tasks on smart speakers was suggested to be associated with cognitive scores of neuropsychological tests used for screening of dementia ( 34 ), while linguistic features extracted from conversational data of phone calls were indicated as significant indicators for differentiating AD patients from cognitively-normal (CN) older adults ( 35 ). These approaches focusing on speech data that can be collected from everyday situations would increase opportunities for assessment and help with the early detection of AD.…”

Section: Introductionmentioning

confidence: 99%

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Yamada

Shinkawa

Kobayashi

et al. 2021

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

show abstract

Section: Introductionmentioning

confidence: 99%

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Yamada

Shinkawa

Kobayashi

et al. 2021

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…As reported in a previous study, speech disfluency can represent an accessibility barrier to voice assistants. For instance, long hesitations or pauses can be misinterpreted by the system as a sentence delimiter [ 23 ], causing an alteration of speech segmentation. Moreover, users must be able to correctly articulate words, especially multisyllable words (eg, temperature) or specific words that may require more effort to be articulated [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

et al. 2020

View full text Add to dashboard Cite

Background Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

show abstract

“…A recent overview of voice user interfaces for elderly can be found in [31]. Currently, there is relatively little research, which takes age into account when building voice interfaces, as confirmed by Stigall et al In a study presented in [16] it was researched how age-related cognitive decline influenced the use of voice interfaces. Several implications for speech assistants, like longer pauses between words, are mentioned, which should be considered in future systems.…”

Section: Resultsmentioning

confidence: 99%

“…In the future Mozilla's TTS 15 using deep learning methods could be an appropriate choice. Together with their ASR these components are intended to be integrated in web browsers as part of the Web Speech API16 .…”

mentioning

confidence: 99%

Development of a Privacy-By-Design Speech Assistant Providing Nutrient Information for German Seniors

Seiderer

Ritschel

André

2020

Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good

View full text Add to dashboard Cite

Elderly people could take benefit of speech assistants since they provide the most natural way to interact with assistive technology. Nevertheless, current speech assistants are mainly based on cloud-systems which are non-functional without a stable internet connection or introduce severe privacy-concerns. In this work we provide an overview of state-of-the-art available components for developing a privacy-by-design, open source speech assistant for seniors, as well as a fully functional implementation. We chose the health related task of getting nutrition information of packaged foodstuff for which we rely on an open data database. Our prototype was used in a workshop with two German seniors to gather further insights into the special requirements of elderly people, which can be taken into account for future speech assistants. CCS CONCEPTS • Human-centered computing → Natural language interfaces; • Applied computing → Consumer health; • Social and professional topics → Seniors.

show abstract

Effects of Age-Related Cognitive Decline on Elderly User Interactions with Voice-Based Dialogue Systems

Cited by 36 publications

References 60 publications

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study

Development of a Privacy-By-Design Speech Assistant Providing Nutrient Information for German Seniors

Contact Info

Product

Resources

About