  This paper presents a fully automated approach for identifying speech
anomalies from voice recordings to aid in the assessment of speech impairments.
By combining Connectionist Temporal Classification (CTC) and
encoder-decoder-based automatic speech recognition models, we generate rich
acoustic and clean transcripts. We then apply several natural language
processing methods to extract features from these transcripts to produce
prototypes of healthy speech. Basic distance measures from these prototypes
serve as input features for standard machine learning classifiers, yielding
human-level accuracy for the distinction between recordings of people with
aphasia and a healthy control group. Furthermore, the most frequently occurring
aphasia types can be distinguished with 90% accuracy. The pipeline is directly
applicable to other diseases and languages, showing promise for robustly
extracting diagnostic speech biomarkers.
