ABSTRACT
Purpose: Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods: The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality: This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings: The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).
ABSTRACT
Procrastination refers to the voluntary avoidance or postponement of action that needs to be taken, that results in negative consequences such as low academic performance, anxiety, and low self-esteem. Previous work has demonstrated the role of social networking site (SNS) design in users' procrastination and revealed several types of procrastination on SNS. In this work, we propose a method to combat procrastination on SNS (D-Crastinate). We present the theories and approaches that informed the design of D-Crastinate method and its stages. The method is meant to help users to identify the type of procrastination they experience and the SNS features that contribute to that procrastination. Then, based on the results of this phase, a set of customised countermeasures are suggested for each user with guidelines on how to apply them. To evaluate our D-Crastinate method, we utilised a mixed-method approach that included a focus group, diary study and survey. We evaluate the method in terms of its clarity, coverage, efficiency, acceptance and whether it helps to increase users' consciousness and management of their own procrastination. The evaluation study involved participants who self-declared that they frequently procrastinate on SNS. The results showed a positive impact of D-Crastinate in increasing participants' awareness and control over their procrastination and, hence, enhancing their digital wellbeing.
