An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients

Mahmoud, S.A.; Kumar, Akshay; Tang, Yiting; Li, Youcun; Gu, Xudong; Fu, Jianming; Fang, Qiang

doi:10.1109/jbhi.2020.3011104

Cited by 28 publications

(29 citation statements)

References 35 publications

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“…Furthermore, these studies reported on the attractiveness and satisfaction when users are playing a serious game to rehabilitate aphasia. In the studies using computer-based solutions to classify aphasia [14,15], techniques of machine learning and DL have been applied. With these works, we have learned about the best classification techniques and audio features to classify aphasia.…”

Section: Literature Search and Questionnairementioning

confidence: 99%

“…At this stage of the research project, we went through a process of developing a model for classifying aphasia. For the development of this model, we will use the DL technique since, according to previous studies, it presents better results to classify aphasia through acoustic data [14,15]. In all, 3 DL algorithms will be used, namely: LeNet, Resnet-34, and SqueezeNet.…”

Section: Algorithms and Model Trainingmentioning

confidence: 99%

“…DL refers to the use of artificial neural networks with multiple hidden layers [26]. The use of DL with vocal data to classify aphasia has been the objective of studies, both to classify the type of aphasia [14] and to estimate severity [15]. Such studies suggest that DL models using vocal data can estimate aphasia in patients with early-stage acute stroke.…”

Section: Hypothesis and Significancementioning

confidence: 99%

“…To make the diagnosis of aphasia simpler and more precise, computational solutions have been developed to classify/detect the types of aphasia. The proposed computational techniques have used classical machine learning [9][10][11][12][13], deep learning (DL) [14,15], and fuzzy logic [16,17]. DL techniques have shown better results in different performance metrics to classify aphasia [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed computational techniques have used classical machine learning [9][10][11][12][13], deep learning (DL) [14,15], and fuzzy logic [16,17]. DL techniques have shown better results in different performance metrics to classify aphasia [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Telemedicine Platform for Aphasia: Protocol for a Development and Usability Study

Nunes¹,

Teles²,

Farias³

et al. 2022

JMIR Res Protoc

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Background Aphasia is a central disorder of comprehension and expression of language that cannot be attributed to a peripheral sensory deficit or a peripheral motor disorder. The diagnosis and treatment of aphasia are complex. Interventions that facilitate this process can lead to an increase in the number of assisted patients and greater precision in the therapeutic choice by the health professional. Objective This paper describes a protocol for a study that aims to implement a computer-based solution (ie, a telemedicine platform) that uses deep learning to classify vocal data from participants with aphasia and to develop serious games to treat aphasia. Additionally, this study aims to evaluate the usability and user experience of the proposed solution. Methods Our interactive and smart platform will be developed to provide an alternative option for professionals and their patients with aphasia. We will design 2 serious games for aphasia rehabilitation and a deep learning–driven computational solution to aid diagnosis. A pilot evaluation of usability and user experience will reveal user satisfaction with platform features. Results Data collection began in June 2022 and is currently ongoing. Results of system development as well as usability should be published by mid-2023. Conclusions This research will contribute to the treatment and diagnosis of aphasia by developing a telemedicine platform based on a co-design process. Therefore, this research will provide an alternative method for health care to patients with aphasia. Additionally, it will guide further studies with the same purpose. International Registered Report Identifier (IRRID) PRR1-10.2196/40603

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Section: Literature Search and Questionnairementioning

confidence: 99%

Section: Algorithms and Model Trainingmentioning

confidence: 99%