Unsupervised Paraphasia Classification in Aphasic Speech

Sharan Pai, Nikhil Sachdeva, Prince Sachdeva, Rajiv Ratn Shah


Abstract
Aphasia is a speech and language disorder which results from brain damage, often characterized by word retrieval deficit (anomia) resulting in naming errors (paraphasia). Automatic paraphasia detection has many benefits for both treatment and diagnosis of Aphasia and its type. But supervised learning methods cant be properly utilized as there is a lack of aphasic speech data. In this paper, we describe our novel unsupervised method which can be implemented without the need for labeled paraphasia data. Our evaluations show that our method outperforms previous work based on supervised learning and transfer learning approaches for English. We demonstrate the utility of our method as an essential first step in developing augmentative and alternative communication (AAC) devices for patients suffering from aphasia in any language.
Anthology ID:
2020.acl-srw.3
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2020
Address:
Online
Editors:
Shruti Rijhwani, Jiangming Liu, Yizhong Wang, Rotem Dror
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–19
Language:
URL:
https://aclanthology.org/2020.acl-srw.3
DOI:
10.18653/v1/2020.acl-srw.3
Bibkey:
Cite (ACL):
Sharan Pai, Nikhil Sachdeva, Prince Sachdeva, and Rajiv Ratn Shah. 2020. Unsupervised Paraphasia Classification in Aphasic Speech. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 13–19, Online. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Paraphasia Classification in Aphasic Speech (Pai et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.acl-srw.3.pdf
Video:
 http://slideslive.com/38928640