In this section
@inproceedings{Sanguedolce:2023:v1/2023.clinicalnlp-1.24,
author = {Sanguedolce, G and Naylor, PA and Geranmayeh, F},
doi = {v1/2023.clinicalnlp-1.24},
pages = {182--190},
publisher = {Association for Computational Linguistics},
title = {Uncovering the potential for a weakly supervised end-to-end model in recognising speech from patient with post-stroke aphasia},
url = {http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.24},
year = {2023}
}
TY - CPAPER
AB - Post-stroke speech and language deficits (aphasia) significantly impact patients' quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p < 0.001). We demonstrated that worse WER was related to the more severe aphasia as measured by expressive (overt naming, and spontaneous speech production) and receptive (written and spoken comprehension) language assessments. Stroke lesion size did not affect the performance of Whisper. Linear mixed models accounting for demographic factors, therapy duration, and time since stroke, confirmed worse Whisper performance with left hemispheric frontal lesions. We discuss the implications of these findings for how future ASR can be improved in PWA.
AU - Sanguedolce,G
AU - Naylor,PA
AU - Geranmayeh,F
DO - v1/2023.clinicalnlp-1.24
EP - 190
PB - Association for Computational Linguistics
PY - 2023///
SP - 182
TI - Uncovering the potential for a weakly supervised end-to-end model in recognising speech from patient with post-stroke aphasia
UR - http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.24
UR - http://hdl.handle.net/10044/1/107750
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom