  Aphasia is a language disorder that can lead to speech errors known as
paraphasias, which involve the misuse, substitution, or invention of words.
Automatic paraphasia detection can help those with Aphasia by facilitating
clinical assessment and treatment planning options. However, most automatic
paraphasia detection works have focused solely on binary detection, which
involves recognizing only the presence or absence of a paraphasia. Multiclass
paraphasia detection represents an unexplored area of research that focuses on
identifying multiple types of paraphasias and where they occur in a given
speech segment. We present novel approaches that use a generative pretrained
transformer (GPT) to identify paraphasias from transcripts as well as two
end-to-end approaches that focus on modeling both automatic speech recognition
(ASR) and paraphasia classification as multiple sequences vs. a single
sequence. We demonstrate that a single sequence model outperforms GPT baselines
for multiclass paraphasia detection.
