Target word prediction and paraphasia classification in spoken discourse

Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman, Jan van Santen


Abstract
We present a system for automatically detecting and classifying phonologically anomalous productions in the speech of individuals with aphasia. Working from transcribed discourse samples, our system identifies neologisms, and uses a combination of string alignment and language models to produce a lattice of plausible words that the speaker may have intended to produce. We then score this lattice according to various features, and attempt to determine whether the anomalous production represented a phonemic error or a genuine neologism. This approach has the potential to be expanded to consider other types of paraphasic errors, and could be applied to a wide variety of screening and therapeutic applications.
Anthology ID:
W17-2301
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://www.aclweb.org/anthology/W17-2301
DOI:
10.18653/v1/W17-2301
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2301.pdf