Publication
Automatic Quantitative Prediction of Severity in Fluent Aphasia Using Sentence Representation Similarity
Katherine Dunfield; Günter Neumann
In: Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments. Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments (RaPID-2020), located at LREC, May 11-16, ISBN 979-10-95546-45-0, LREC, 2020.
Abstract
Aphasia is a neurological language disorder that can severely impair a person’s language production or comprehension abilities. Due
to the nature of impaired comprehension, as well as the lack of substantial annotated data of aphasic speech, quantitative measures of
comprehension ability in aphasic individuals are not easily obtained directly from speech. Thus, the severity of some fluent aphasia
types has remained difficult to automatically assess. We investigate six proposed features to capture symptoms of fluent aphasia —
three of which are focused on aspects of impaired comprehension ability, and evaluate them on their ability to model aphasia severity.
To combat the issue of data sparsity, we exploit the dissimilarity between aphasic and healthy speech by leveraging word and sentence
representations from a large corpus of non-aphasic speech, with the hypothesis that conversational dialogue contains implicit signifiers
of comprehension. We compare results obtained using different regression models, and present proposed feature sets which correlate
(best Pearson p = 0.619) with Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ). Our experiments further demonstrate
that we can achieve an improvement over a baseline through the addition of the proposed features for both WAB-R AQ prediction and
Auditory-Verbal Comprehension WAB sub-test score prediction.