A Machine Learning Based System for the Automatic Evaluation of Aphasia Speech

Aphasia is an acquired language disorder resulting from damage to language related networks of the brain, most often as a result of ischemic stroke or traumatic brain injury. Within the European Union, over 580 000 people are affected each year. Both assessment and treatment of aphasia require the analysis of language, in particular of spontaneous speech. Factoring in therapy and diagnosis sessions, which require the presence of a speech therapist and a physician, aphasia is a resource intensive condition: It has been estimated that in Germany alone, there are 70 000 new cases of stroke-related aphasia every year, 35 000 of which persist over more than six months - all of which should receive formal diagnostic testing at some point. Having an automatic system for the detection and evaluation of aphasic speech would be of great benefit for the medical domain by immensely speeding up diagnostic processes and thus freeing up valuable resources for, e.g., therapy. As a first step towards building such a system, it is necessary to identify the vocal biomarkers which characterize aphasic speech. Furthermore, a database is needed which maps from recordings of aphasic speech to the type and severity of the disorder. In this paper, we present the vocal biomarkers and a description of the existing Aachen Aphasia database containing recordings and transcriptions of therapy sessions. We outline how the biomarkers and the database could be used to construct a recognition system which automatically maps pathological speech to aphasia type and severity.