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,
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://aclanthology.org/W17-2301
DOI:
10.18653/v1/W17-2301
Bibkey:
Cite (ACL):
Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman, and Jan van Santen. 2017. Target word prediction and paraphasia classification in spoken discourse. In BioNLP 2017, pages 1–8, Vancouver, Canada,. Association for Computational Linguistics.
Cite (Informal):
Target word prediction and paraphasia classification in spoken discourse (Adams et al., BioNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2301.pdf