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 severity7,11,12,15,17-20 (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 letter11,12 (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 controls6,9,26,30 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 controls20,31,39 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 controls6,7,26 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 words7,36 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
