@inproceedings{pai-etal-2020-unsupervised,
title = "Unsupervised Paraphasia Classification in Aphasic Speech",
author = "Pai, Sharan and
Sachdeva, Nikhil and
Sachdeva, Prince and
Shah, Rajiv Ratn",
editor = "Rijhwani, Shruti and
Liu, Jiangming and
Wang, Yizhong and
Dror, Rotem",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-srw.3/",
doi = "10.18653/v1/2020.acl-srw.3",
pages = "13--19",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Paraphasia Classification in Aphasic Speech
%A Pai, Sharan
%A Sachdeva, Nikhil
%A Sachdeva, Prince
%A Shah, Rajiv Ratn
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F pai-etal-2020-unsupervised
%X 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.
%R 10.18653/v1/2020.acl-srw.3
%U https://aclanthology.org/2020.acl-srw.3/
%U https://doi.org/10.18653/v1/2020.acl-srw.3
%P 13-19
Markdown (Informal)
[Unsupervised Paraphasia Classification in Aphasic Speech](https://aclanthology.org/2020.acl-srw.3/) (Pai et al., ACL 2020)
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.