Sensors, Vol. 23, Pages 857: A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries

Sensors, Vol. 23, Pages 857: A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries Sensors doi: 10.3390/s23020857 Authors: Seedahmed S. Mahmoud Raphael F. Pallaud Akshay Kumar Serri Faisal Yin Wang Qiang Fang The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research