  Paraphasias are speech errors that are often characteristic of aphasia and
they represent an important signal in assessing disease severity and subtype.
Traditionally, clinicians manually identify paraphasias by transcribing and
analyzing speech-language samples, which can be a time-consuming and burdensome
process. Identifying paraphasias automatically can greatly help clinicians with
the transcription process and ultimately facilitate more efficient and
consistent aphasia assessment. Previous research has demonstrated the
feasibility of automatic paraphasia detection by training an automatic speech
recognition (ASR) model to extract transcripts and then training a separate
paraphasia detection model on a set of hand-engineered features. In this paper,
we propose a novel, sequence-to-sequence (seq2seq) model that is trained
end-to-end (E2E) to perform both ASR and paraphasia detection tasks. We show
that the proposed model outperforms the previous state-of-the-art approach for
both word-level and utterance-level paraphasia detection tasks and provide
additional follow-up evaluations to further understand the proposed model
behavior.
