UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
An utterance verification system for word naming therapy in Aphasia
  • Publication Type:
    Conference
  • Authors:
    Barbera DS, Huckvale M, Fleming V, Upton E, Coley-Fisher H, Shaw I, Latham W, Leff AP, Crinion J
  • Publication date:
    29/10/2020
  • Pagination:
    706, 710
  • Published proceedings:
    Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
  • Volume:
    2020-October
  • Status:
    Published
  • Name of conference:
    Interspeech 2020
  • Print ISSN:
    2308-457X
Abstract
© 2020 ISCA Anomia (word finding difficulties) is the hallmark of aphasia an acquired language disorder, most commonly caused by stroke. Assessment of speech performance using pijcture naming tasks is therefore a key method for identification of the disorder and monitoring patient's response to treatment interventions. Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in ASR and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present an utterance verification system incorporating a deep learning element that classifies 'correct'/'incorrect' naming attempts from aphasic stroke patients. When tested on 8 native British-English speaking aphasics the system's performance accuracy ranged between 83.6% to 93.6%, with a 10 fold cross validation mean of 89.5%. This performance was not only significantly better than one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Institute of Cognitive Neuroscience
Author
Speech, Hearing & Phonetic Sciences
Author
Brain Repair & Rehabilitation
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by