Using Automatic Assessment of Speech Production to Predict Current and Future Cognitive Function in Older Adults

With your good chance of getting dementia this test should be prescribed by your doctor to establish a baseline for you. And then if found implement THOSE EXACT DEMENTIA PREVENTION PROTOCOLS  your doctor should have competently already set up.

Your risk of dementia, has your doctor told you of this?

1. A documented 33% dementia chance post-stroke from an Australian study?   May 2012.

2. Then this study came out and seems to have a range from 17-66%. December 2013.`    

3. A 20% chance in this research.   July 2013.

4. Dementia Risk Doubled in Patients Following Stroke September 2018 

The latest here:

 

Using Automatic Assessment of Speech Production to Predict Current and Future Cognitive Function in Older Adults

Rachel Ostrand, PhD, John Gunstad, PhD

First Published July 29, 2020 Research Article Find in PubMed 

https://doi.org/10.1177/0891988720933358

Article information

Abstract

Neurodegenerative conditions like Alzheimer disease affect millions and have no known cure, making early detection important. In addition to memory impairments, dementia causes substantial changes in speech production, particularly lexical-semantic characteristics. Existing clinical tools for detecting change often require considerable expertise or time, and efficient methods for identifying persons at risk are needed. This study examined whether early stages of cognitive decline can be identified using an automated calculation of lexical-semantic features of participants’ spontaneous speech. Unimpaired or mildly impaired older adults (N = 39, mean 81 years old) produced several monologues (picture descriptions and expository descriptions) and completed a neuropsychological battery, including the Modified Mini-Mental State Exam. Most participants (N = 30) returned one year later for follow-up. Lexical-semantic features of participants’ speech (particularly lexical frequency) were significantly correlated with cognitive status at the same visit and also with cognitive status one year in the future. Thus, automated analysis of speech production is closely associated with current and future cognitive test performance and could provide a novel, scalable method for longitudinal tracking of cognitive health.

Keywords

dementia, cognitive decline, 3MS, lexical frequency, automated assessment, early detection

Introduction

Alzheimer disease (AD) and other forms of dementia are epidemic. With the aging of the baby boomer generation, the prevalence of AD will be more than triple by 2050 and generate US$1.1 trillion in annual health care costs in the United States alone.¹ With no known cure, early detection is essential for disease management. Detecting early signs of cognitive impairment often involves initial screening during a primary care or neurological evaluation, followed by comprehensive neuropsychological testing of cognitive function. Administration and interpretation of these assessments require substantial expertise and time, and thus cannot be easily conducted at regular intervals to identify and track people at risk. Additionally, screening techniques that can only be found in clinical settings may provide a barrier to individuals with reduced access to medical care or persons reluctant to discuss concerns about memory loss. New methods for early detection that can be delivered in community settings or at home could allow individuals to monitor cognitive status over time and then seek a comprehensive evaluation with their providers (e.g., neurological evaluation, neuroimaging, and neuropsychological testing) at the earliest signs of decline.

Prior work has shown that AD and other forms of dementia are associated with measurable changes in the affected person’s speech production. In particular, during early stages of cognitive decline, word-finding and semantic knowledge become more difficult, likely due to a combination of the degradation of the stored meaning of words and the process of lexical access (for reviews, see studies by Kemper & Altmann,² Burke & Shafto,³ and Obler & Albert⁴). This degradation induces reduced semantic specificity, including vague and empty words, higher-frequency words with less precise meanings, and indefinite articles (e.g., see studies by Bird et al.,⁵ Croisile et al.,⁶ Hier et al.,⁷ Feyereisen et al.,⁸ and Nicholas et al.⁹). In effect, early-stage dementia reduces the amount of specific content information conveyed during speech, while maintaining contextual relevance and grammaticality. In contrast, other levels of linguistic processing, including articulatory production, phonetic retrieval, and syntax, remain largely unimpaired until much more advanced stages of the disease (e.g., see studies by Hier et al.,⁷ Bayles et al.,¹⁰ Forbes-McKay et al.,¹¹ Salvatierra et al.,¹² Hoffmann et al.,¹³ and Filiou et al.¹⁴).

This relative impairment of lexical-semantics is particularly true during the early stages of dementia, a time when other behavioral symptoms are often not yet noticeable. This suggests that measuring lexical-semantic properties of an at-risk person’s speech production may be an effective way to identify and track early-stage cognitive decline. The present study investigates whether certain automatically calculable lexical-semantic features of spontaneous speech are reliable indicators of cognitive decline, and if spontaneous speech could be used for frequent, longitudinal assessment of people at risk for dementia. We do so by assessing whether a set of lexical-semantic features which are calculated automatically from a sample of spontaneous speech can predict current and future neuropsychological test scores.

Linguistic Changes in AD and Related Dementias

There is substantial research documenting linguistic changes in people with neurodegenerative disorders. In particular, in early-stage mild cognitive impairment (MCI) and dementia, lexical access, word retrieval, and other types of semantic knowledge become more difficult (for reviews, see studies by Burke & Shafto³ and Obler & Albert⁴), stemming from the degradation of both the semantic meaning of particular words in the lexicon in addition to a disruption to the process of lexical access.² Behaviorally, this is manifest in speech production as a reduction in semantic specificity, causing an increase in vague, generic, or empty words, higher-frequency words with less precise meanings, and increased use of indefinite articles and anaphora (e.g., see studies by Bird et al.,⁵ Croisile et al.,⁶ Hier et al.,⁷ Feyereisen et al.,⁸ and Nicholas et al.⁹). This reduction in semantic specificity means that speakers produce less specific content information while still maintaining the overall semantic context and grammaticality. In contrast, other levels of linguistic processing remain largely unimpaired, as demonstrated by oral reading, writing to dictation, word repetition, and phonemic fluency tasks that are nearly on par with those of unimpaired controls.^10-12,15

Research on the changes in speech production among people with dementia has, broadly speaking, been conducted using two types of methodologies. One method is using controlled elicitation tasks—where the participant is given some stimulus and is asked to produce small bits of speech in response. These types of tasks include confrontation naming, when the participant is shown a series of pictures and asked to give their names (e.g., the Boston Naming Test), and semantic or phonemic fluency, when the participant is given a category or a letter and asked to provide as many words as they can which belong to that category or start with that letter, among others. The constraint of controlled tasks is both a benefit and a disadvantage. It makes quantification of a participant’s performance and comparison against other patients or established norms easier, as there is a specific, predetermined metric to measure, such as the number of words produced in the given category. On the other hand, these tasks only measure a small slice of the participant’s linguistic and cognitive abilities and do not measure a person’s language abilities in a way that is similar to everyday language use.

The other method to assess speech production is via spontaneous speech elicitation tasks—a prompt which allows the participant to speak in a relatively unconstrained manner and produce connected speech, such as describing a picture or answering an open-ended question. With these types of tasks, the lack of constraint is both a benefit and a disadvantage. They can assess the participant’s abilities and deficits in a situation which is much more relevant to real communication skills in their life, quantifying a major challenge with the disease,¹⁶ and also allow a wide range of linguistic properties to be measured at multiple linguistic levels, including word choice, semantic content, syntactic structures, and acoustic properties. This measurement flexibility is a downside, however, as it is less straightforward to compare deficits between participants and to determine which metrics are the appropriate ones to measure.

Linguistic Changes on Controlled Elicitation Tasks

Controlled elicitation tasks are a good way to assess deficits on a specific linguistic characteristic. On semantic fluency tasks, where participants name as many exemplars of a category (e.g., animals) that they can, persons with AD perform significantly below the level of age-matched controls, and degree of impairment is correlated with clinically assessed dementia severity^{7,11,12,15,17-20} (see study by Henry et al.²¹ for a review). Patients with AD also perform significantly below the level of sex- and education-matched controls on verb fluency tasks, in which they need to specifically access words for “things that people do,” and verb naming tasks, in which they are shown a picture and are asked to say what the person is doing or what is happening in the picture.²²

People with AD are also impaired on explicit lexical access tasks. For example, patients with AD are impaired compared to healthy controls on confrontation naming, when they are shown a picture of an object and asked to produce its name.^{7,10,11,15,18,20} In addition, participants with dementia score lower than healthy controls on the Wechsler Adult Intelligence Scale - 4 (WAIS) vocabulary subtest, which tests participants’ ability to provide a concise definition for a given word.⁷

However, these linguistic deficits seem to be primarily focused on lexical and semantic processes. For example, although people with AD were impaired on semantic fluency tasks, they are relatively unimpaired compared to controls on similar phonemic fluency tasks, in which they are asked to produce as many words as they can which begin with a particular letter^11,12 (but see study by Sajjadi et al.¹⁵ for different results). In a cuing task, participants were asked to write from dictation a pair of words. The second word was a homophone (e.g., nose/knows); the first word provided a cue as to which lexical item of the homophone pair was intended. The cue word was either semantically (thinks) or syntactically (she) related to the target homophone (knows). Although controls performed the task with the same accuracy for the two types of cues, people with AD made significantly more errors when given a semantic cue rather than a syntactic cue, suggesting that semantic lexical access is particularly impaired in AD.²³ Similarly, on various neuropsychological tests, including the subtests of the Boston Diagnostic Aphasia Exam, participants with MCI and mild AD are more impaired on semantic and general fluency tasks,²⁴ and these tasks tend to be the most discriminative between early-stage patients and healthy controls.

Thus, evidence from controlled, experimental tasks points toward the linguistic deficits which occur in dementia, particularly in MCI and early-stage AD, as being those involving lexical-semantic processes. Research measuring patients’ spontaneous speech shows similar types of deficits, as discussed in the following section.

Linguistic Changes on Spontaneous Speech Tasks

There is also some prior research which has investigated AD or MCI patients’ linguistic deficits on spontaneous speech tasks (for reviews, see studies by Filiou et al.,¹⁴ Boschi et al.,²⁵ Kavé & Goral,²⁶ Slegers et al.,²⁷ and Mueller et al.²⁸). The most common spontaneous speech elicitation task is picture description, when the participant is shown a relatively complex scene and asked to describe in detail what is occurring. A popular picture for these types of tasks is the Cookie Theft from the Boston Diagnostic Aphasia Exam.²⁹ Other tasks include short writing prompts, semi-structured interviews, in which the experimenter prompts the participant with only occasional questions to keep the speech largely monologue, and retelling a story.²⁵ Spontaneous speech tasks demonstrate similar deficits as do controlled experimental tasks: Particularly in the early stages of cognitive decline, participants show impairment on lexical access and measures of semantic specificity, and relatively intact syntax and phonological and phonetic production.

A notable change in spontaneous speech in people with dementia is the increased production of empty or indefinite words as compared to healthy controls^6,9,26,30 or from early- to late-stage impairment.⁷ These are words that are highly vague and nonspecific, like “thing” and “stuff,” and their production may indicate word-finding difficulty or semantic memory degradation, as the speaker is attempting to refer to a particular entity without being able to access its name. The more general metric of this type of word-finding difficulty is manifest in average lexical frequency, as people with dementia produce more common, higher-frequency words than do healthy controls, and average lexical frequency increases as the patient’s disease progresses.^5,20,31 Low-frequency words are more difficult to access from the lexicon, and less specific words (thing) tend to be substantially higher-frequency than a more specific counterpart (cookie). People with AD and cognitive decline also show reduced lexical diversity, repeating the same words rather than producing unique words,^7,32-34 a behavior which increases with disease progression,^35,36 another manifestation of their reduced ability to easily retrieve words from their lexicon.

A related linguistic deficit in MCI and AD is the reduction of content words in spontaneous speech as compared to controls.^6,8,20,37 Content words convey semantic meaning (as contrasted with function words, which convey grammatical relationships and largely provide syntactic scaffolding) and include nouns, verbs, and adjectives (in contrast to, e.g., pronouns and prepositions). As with the previous metrics, the reduction of content word production in MCI and AD is likely an indicator of a lexical access deficit and is a metric of reduced semantic specificity. In fact, as dementia severity worsens, the production of content words is even further reduced.³⁸ Conversely, impaired participants produce increased function words,¹⁵ particularly by replacing nouns (which are more specific) with pronouns (which are vaguer and less explicit), compared to controls^20,31,39 and as the degree of impairment increases.⁷ People with AD also make fewer definite references to objects compared to controls,⁸ a deficit which is thought to index impairments in declarative memory.⁴⁰

Some studies report that patients produce fewer total words than controls^6,7,26 or that total word count decreases as impairment increases.^35,36 However, although many studies have found a numeric decrease in word count among impaired participants, the difference between groups or as a function of dementia severity is often not significant.^{8,15,20,34,38,39,41} However, in referential communication dialogue tasks, which require the participant to adapt their speech based on learning from repeated interaction with a partner, people with AD actually produce more words than controls because they do not seem to learn and match their partner’s language use.^8,41 Persons with AD also speak more slowly than controls in spontaneous speech, and speech rate anticorrelates with impairment severity,^13,15,41,42 with longer pauses and more word-finding delays among more impaired people.^6,13,26,43 Relatedly, people even with mild AD produce more filler words (filled pauses such as “um” and “uh”) as compared to unimpaired older adults, suggesting the patients may be struggling with rapid lexical access.¹⁵

Lastly, patients with MCI or AD produce fewer words that are important to the discourse, and the words they do produce tend to be less relevant than those produced by elderly healthy controls. Much prior work investigating dementia patients’ production of spontaneous speech (usually describing a picture which has fixed visual features) has counted the number of information or content units produced by the speaker, which can be compared against the “correct” number for that picture. Persons with dementia produce significantly fewer content units about the setting, events, characters, and the main idea of a scene compared to controls,^{6,8-10,15,31,37} a deficit which is apparent even in very early-stage MCI,³⁴ and the degree of reduction is a function of dementia severity.⁴⁴ In both monologue and dialogue, more impaired participants produce fewer pieces of crucial information⁴¹ and a lower density of relevant information out of the total narrative they produce.^36,45 Conversely, people with AD produce more irrelevant information: words that are linguistically correct but not appropriate or useful to the current context.^6,41,43,45 Relatedly, more impaired patients’ speech has lower idea density and higher uninformative output, producing fewer distinct pieces of information per number of words^7,36 and also per time unit,^41,45 and such deficits are evident in their writing as well.^38,46 This is also manifest in dialogue, when people with AD require more speaking turns to convey their message to their listener.⁸ Together, these observations suggest that although impaired speakers may not reduce their total lexical output, what they do produce is wordier and less precise and conveys less information about the topic at hand. This suggests that patients, particularly with early-stage dementia, produce reduced semantic content and specificity, and these characteristics of speech could be used to quantify a person’s degree of impairment. We assess this possibility in the present work, using automated calculation of lexical-semantic characteristics of spontaneous speech.

Can Spontaneous Speech Be Analyzed Automatically to Detect Cognitive Impairment?

The present work investigates whether semantic specificity in spontaneous speech can predict the degree of cognitive decline in older adults without a diagnosis of dementia. As mentioned above, current screening methods for cognitive impairment often require a visit with a clinician or trained examiner, potentially including administration of a battery of neuropsychological tests. This necessitates travel, access to medical care, substantial time for evaluation, and specially trained medical personnel. In addition, some screening tests have a restricted set of stimuli and thus frequent repetition in a short time span may artificially inflate scores. Together, these reasons encourage the development of additional methodologies for monitoring an individual patient’s cognitive status over time.

The goal of the present work is not to combine language features with scores from traditional assessments to better predict future neuropsychological scores. Instead, we hope to demonstrate that lexical-semantic features of spontaneous speech can serve as a proxy measure for clinical cognitive screening instruments and thus could be administered in between clinical visits or before a person thinks to be evaluated the first time. Spontaneous speech can be collected in a participant’s home, with minimal equipment and training, only a small outlay of time (minutes rather than hours), and using a much more varied set of materials to elicit responses from participants. Spontaneous speech thus has potential as a tool which can be collected with minimal burden on the participant. In addition, the features discussed here are straightforward to calculate automatically from transcribed speech. Thus, the present work seeks to demonstrate a predictive relationship between linguistic features and clinical cognitive status in older adults, to ultimately demonstrate that speech could itself be a useful diagnostic metric, opening the door to substantially more frequent assessment and monitoring of at-risk people in between clinical examinations. Additionally, understanding the components of speech that are particularly informative of cognitive status can help elucidate the organization of language in the brain; in particular, demonstrating which characteristics track with damage induced by neurodegeneration over time can aid our understanding of how neurodegenerative conditions progress.

Here, we make use of the known relationship between cognitive status and speech production, except in the opposite causal direction. Rather than studying how cognitive status affects a particular linguistic characteristic, we investigate whether we can use changes in linguistic characteristics to predict changing cognitive status. In addition, in contrast to much prior research which compared different individuals’ behaviors against each other to classify them as healthy or impaired, we employ a within-subjects, longitudinal design, and use a particular individual’s speech production at an initial visit to predict their own cognitive status at that visit and also one year in the future. This avoids confounds of between-subjects differences in education level, socioeconomic status, language proficiency, and so on.

There is some precedent for this automatic approach in prior work, as several studies have collected spontaneous speech samples of participants with the ultimate goal of predicting participants’ cognitive scores. Kavé and Dassa⁴⁷ found that the number of complete words spoken, type-token ratio, average lexical frequency, and the number of information units each individually correlated with Mini-Mental State Exam (MMSE) scores in persons with AD. Bucks and colleagues³² collected speech samples from participants with AD and matched healthy controls via a semi-structured interview session, and found differences between AD and controls on several linguistic measures including part-of-speech counts and vocabulary richness, and their model classified participants (AD vs. control) with 87.5% accuracy. Ahmed and colleagues³⁷ found significant differences in spontaneous speech between healthy controls and people with autopsy-confirmed AD and also between participants at different stages of AD, on the proportion of pronouns and verbs produced, and a composite semantic and information content measure. Fraser et al.³¹ took a computational approach to distinguishing participants with AD from healthy controls using spontaneous speech from picture descriptions. They calculated 370 linguistic features—lexical, semantic, information content, syntactic, and acoustic—and used machine learning to classify AD versus controls with up to 82% accuracy.

The Present Approach

However, there are a number of limitations in these prior studies using automatic assessment of spontaneous speech as a diagnostic measure of cognitive decline, which we address in several important ways. First, we use linguistic features to predict a cognitive score for all participants—both healthy and impaired—in order to characterize individuals along the continuous spectrum of cognitive decline. This is in contrast to most existing work that only predicted outcomes for already-impaired participants (e.g., a study by Kavé & Dassa⁴⁷) and/or merely conducted a binary by-group classification to discriminate patients from controls, rather than predicting the degree of impairment (e.g., see studies by Fraser et al.,³¹ Bucks et al.,³² Ahmed et al.,³⁷ and Asgari et al.⁴⁸). In the present work, in line with the goal to be clinically relevant, we use linguistic features to assess participants at various stages of impairment (or lack thereof) and predict their neuropsychological score along a continuous scale rather than simply binning participants into the “impaired” or “unimpaired” group. This strategy would allow for a clinician to follow a participant’s progression via his/her speech production and monitor cognitive decline as it occurs.

Second, in line with the goal of longitudinal screening and monitoring of cognitive function across a community, all linguistic features were calculated automatically rather than manually counted or annotated. The only component that was not automated was the speech transcription. (As automatic speech recognition technology improves, even manual transcription will become less necessary.)

Third, some previous studies (e.g., study by Kavé & Dassa⁴⁷) investigated the relationship between disease status and each linguistic feature separately. However, this may miss important relationships between multiple linguistic features that jointly have predictive power of cognitive status, and thus we use a combination of linguistic features as predictors.

Fourth, we restricted the set of linguistic predictor variables to those which are theoretically and experimentally motivated and are human-interpretable (in contrast to, e.g., a study by Fraser et al.³¹). An automated cognitive assessment system meant to be used in conjunction with a clinician’s assessment should be human-explainable to allow clinicians to understand the behavior which drove the automatic assessment.

Finally, we use the Modified Mini-Mental State Exam (3MS) rather than the MMSE as the cognitive screening test. The 3MS increases the score range compared to the MMSE, allowing for greater sensitivity to impairment by including items that test a broader range of cognitive functions. The 3MS has been shown to be better at identifying dementia than the MMSE—both higher sensitivity (detecting true positives) and also higher specificity (detecting true negatives), and is more internally consistent.^49,50

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