The question‐answer adjacency pair in dementia discourse

Suárez, Ana Varela

doi:10.1111/ijal.12185

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…However, many of these features are task-specific, and focus only on the participants' part of the dialog. Nonetheless, fine-grained analysis of questionanswer ratio has been the focus of several studies showing promising performance on dementia detection (Hamilton, 1994;Varela Suárez, 2018).…”

Section: Interaction Patterns and Ad Detectionmentioning

confidence: 99%

Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration

Farzana,

Parde

2022

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Dementia often manifests in dialog through specific behaviors such as requesting clarification, communicating repetitive ideas, and stalling, prompting conversational partners to probe or otherwise attempt to elicit information. Dialog act (DA) sequences can have predictive power for dementia detection through their potential to capture these meaningful interaction patterns. However, most existing work in this space relies on content-dependent features, raising questions about their generalizability beyond small reference sets or across different cognitive tasks. In this paper, we adapt an existing DA annotation scheme for two different cognitive tasks present in a popular dementia detection dataset. We show that a DA tagging model leveraging neural sentence embeddings and other information from previous utterances and speaker tags achieves strong performance for both tasks. We also propose content-free interaction features and show that they yield high utility in distinguishing dementia and control subjects across different tasks. Our study provides a step toward better understanding how interaction patterns in spontaneous dialog affect cognitive modeling across different tasks, which carries implications for the design of non-invasive and low-cost cognitive health monitoring tools for use at scale.

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Section: Interaction Patterns and Ad Detectionmentioning

confidence: 99%

Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration

Farzana,

Parde

2022

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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“…Conversational dialogue is the primary means of human natural language use, so dialogue, and open domain dialogue in particular, might provide more generally applicable insights in studying the effects of AD on dialogue (Nasreen et al, 2019). Conversational analysis (CA) studies have traditionally looked in more detail at what characteristics of dialogue with dementia might be important (Jones et al, 2016;Elsey et al, 2015;Hamilton, 2005;Davis and Maclagan, 2010;Mirheidari et al, 2019;Perkins et al, 1998;Varela Suárez, 2018). Although some computational works explore the detection of dementia from speech and interaction (e.g.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

Nasreen

Rohanian

Hough

et al. 2021

Front. Comput. Sci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder mainly characterized by memory loss with deficits in other cognitive domains, including language, visuospatial abilities, and changes in behavior. Detecting diagnostic biomarkers that are noninvasive and cost-effective is of great value not only for clinical assessments and diagnostics but also for research purposes. Several previous studies have investigated AD diagnosis via the acoustic, lexical, syntactic, and semantic aspects of speech and language. Other studies include approaches from conversation analysis that look at more interactional aspects, showing that disfluencies such as fillers and repairs, and purely nonverbal features such as inter-speaker silence, can be key features of AD conversations. These kinds of features, if useful for diagnosis, may have many advantages: They are simple to extract and relatively language-, topic-, and task-independent. This study aims to quantify the role and contribution of these features of interaction structure in predicting whether a dialogue participant has AD. We used a subset of the Carolinas Conversation Collection dataset of patients with AD at moderate stage within the age range 60–89 and similar-aged non-AD patients with other health conditions. Our feature analysis comprised two sets: disfluency features, including indicators such as self-repairs and fillers, and interactional features, including overlaps, turn-taking behavior, and distributions of different types of silence both within patient speech and between patient and interviewer speech. Statistical analysis showed significant differences between AD and non-AD groups for several disfluency features (edit terms, verbatim repeats, and substitutions) and interactional features (lapses, gaps, attributable silences, turn switches per minute, standardized phonation time, and turn length). For the classification of AD patient conversations vs. non-AD patient conversations, we achieved 83% accuracy with disfluency features, 83% accuracy with interactional features, and an overall accuracy of 90% when combining both feature sets using support vector machine classifiers. The discriminative power of these features, perhaps combined with more conventional linguistic features, therefore shows potential for integration into noninvasive clinical assessments for AD at advanced stages.

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“…Conversational dialogue is the primary means of human natural language use, so dialogue, and open domain dialogue in particular, might provide more generally applicable insights in studying the effects of AD on dialogue (Nasreen et al, 2019). Conversational analysis (CA) studies have traditionally looked in more detail at what characteristics of dialogue with dementia might be important (Jones et al, 2016;Elsey et al, 2015;Hamilton, 2005;Davis and Maclagan, 2010;Perkins et al, 1998;Varela Suárez, 2018). Although some computational works explore the detection of dementia from speech and interaction (e.g.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's Dementia Recognition Through Spontaneous Speech

2021

Frontiers Research Topics

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The question‐answer adjacency pair in dementia discourse

Cited by 5 publications

References 31 publications

Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration

Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration

Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features

Alzheimer's Dementia Recognition Through Spontaneous Speech

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