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Cited by 9Speech and dialogue systemsInnovation in Digital Healthcare SystemsEmotion and Mood Recognition

Exploratory analysis of real personal emergency response call conversations: considerations for personal emergency response spoken dialogue systems

Open access
Young, Victoria, Rochon, Elizabeth, Mihailidis, Alex·2016·Journal of NeuroEngineering and Rehabilitation
DOI 10.1186/s12984-016-0207-9PMC PMC5109662
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Abstract

BACKGROUND: The purpose of this study was to derive data from real, recorded, personal emergency response call conversations to help improve the artificial intelligence and decision making capability of a spoken dialogue system in a smart personal emergency response system. The main study objectives were to: develop a model of personal emergency response; determine categories for the model's features; identify and calculate measures from call conversations (verbal ability, conversational structure, timing); and examine conversational patterns and relationships between measures and model features applicable for improving the system's ability to automatically identify call model categories and predict a target response. METHODS: This study was exploratory and used mixed methods. Personal emergency response calls were pre-classified according to call model categories identified qualitatively from response call transcripts. The relationships between six verbal ability measures, three conversational structure measures, two timing measures and three independent factors: caller type, risk level, and speaker type, were examined statistically. RESULTS: Emergency medical response services were the preferred response for the majority of medium and high risk calls for both caller types. Older adult callers mainly requested non-emergency medical service responders during medium risk situations. By measuring the number of spoken words-per-minute and turn-length-in-words for the first spoken utterance of a call, older adult and care provider callers could be identified with moderate accuracy. Average call taker response time was calculated using the number-of-speaker-turns and time-in-seconds measures. Care providers and older adults used different conversational strategies when responding to call takers. The words 'ambulance' and 'paramedic' may hold different latent connotations for different callers. CONCLUSIONS: The data derived from the real personal emergency response recordings may help a spoken dialogue system classify incoming calls by caller type with moderate probability shortly after the initial caller utterance. Knowing the caller type, the target response for the call may be predicted with some degree of probability and the output dialogue could be tailored to this caller type. The average call taker response time measured from real calls may be used to limit the conversation length in a spoken dialogue system before defaulting to a live call taker.

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  • VUIs may be more appropriate than push-button systems for making emergency calls because natural language interaction is intuitive and some people naturally call for help when stuck on the floor after a fall.

    “In particular, Voice-User Interfaces (VUIs) may be more appropriate than 'push-button' systems for making emergency calls for help because natural language interaction is intuitive (Vacher et al., 2015a; Young et al., 2016) and some people naturally call for help when stuck on the floor after a fall or express vocal cues during the event of a fall (Bobillier-Chaumon et al., 2016).”

    From Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House (2019) · cites this paper as Young et al., 2016

    The Background section on spoken dialogue systems (page unavailable) says speech-based systems mirror human conversation, that older adults were receptive to using speech with personal emergency response systems, and that speech activation could avoid the need to press a worn button. This supports the proposed advantage over push-button systems, but the paper does not establish that natural-language interaction is intuitive. The claim about calling out after a fall is attached to Bobillier-Chaumon et al., not Young et al. (LLM, conf=0.78)

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Research works printed but not linked: 7

  1. [4]McLean MH. Design of a Speech Recognition Interface for a Personal Emergency Response and Health Monitoring System. Master’s Thesis. University of Toronto; 2005.
  2. [47]LaPointe LL. Chapter 9: Neurogenic disorders of communication. In: Minifie, FD, editor. Introduction to communication sciences and disorders. New York: Delmar Cengage Learning; 1994. p. 351–97.
  3. [56]Furui S. Spontaneous speech recognition and summarization. In: Langemets M, Penjam P, editors. Proceedings of the Second Baltic Conference on Human Language Technologies. Tallinn: ISCA; 2005, 39:50–61.
  4. [63]Plano Clark VL, Creswell JW. Chapter 3: Choosing a mixed methods design. In Designing and conducting mixed methods research. 2nd ed. Thousand Oaks, California: Sage Publications; 2011:53–106.
  5. [81]Young V. Content Analyses of Personal Emergency Response Calls: Towards a More Robust Spoken Dialogue-Based Personal Emergency Response System. Doctoral Thesis. University of Toronto; 2016.
  6. [87]Hall N, Wagovich SA, Bernstein Ratner N: Chapter 9 - Language considerations in childhood stuttering, Stuttering and related fluency disorders. In: Conture E, Curlee R, editors. Third ed. Stuttering and related fluency disorders. New York: Thieme; 2007. p. 153–67.
  7. [92]Zraick RI Gregg BA Whitehouse EL Speech and voice characteristics of geriatric speakers: a review of the literature and a call for research and training J Med Speech Lang Pathol 2006 14 133 142

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Authors
Young, V., Rochon, E., & Mihailidis, A.
Affiliations
University Health Network (CA)
Corresponding author
Young, Victoria
ORCID
Venue
Journal of NeuroEngineering and Rehabilitation
Publisher
Springer Science and Business Media LLC
Published
2016-11-14
Volume
13
Issue
1
Pages
97-97
ISSN
1743-0003 (ISSN-L)
DOI
10.1186/s12984-016-0207-9
PMC
PMC5109662
OpenAlex
W2550431173
Cited by
9
References
92
Citations by year
2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2019: 2 · 2018: 1
FWCI
0.82
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • TSpace (University of Toronto) (submittedVersion, cc-by)
  • TSpace (University of Toronto) (submittedVersion, cc-by-nc-nd)
Funders
  • Toronto Rehabilitation Institute
  • University of Toronto
  • Canadian Institutes of Health Research
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
spoken dialogue systems, conversation structure, emergency medical response, conversational strategies, risk level
MeSH terms
Adult, Aged, Communication, Emergency Medical Services, Female, Humans, Language, Male, Pattern Recognition, Automated

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