Unsupervised Paraphasia Classification in Aphasic Speech
| Autor: | Sharan Pai, Rajiv Ratn Shah, Prince Sachdeva, Nikhil Sachdeva |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Speech recognition Supervised learning medicine.disease 01 natural sciences Paraphasia 030507 speech-language pathology & audiology 03 medical and health sciences Augmentative and alternative communication Aphasia 0103 physical sciences medicine ComputingMilieux_COMPUTERSANDSOCIETY Language disorder medicine.symptom 0305 other medical science Transfer of learning 010303 astronomy & astrophysics |
| Zdroj: | ACL (student) |
| DOI: | 10.18653/v1/2020.acl-srw.3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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