Assistive Mobile App for Children with Hearing &amp; Speech Impairment Using Character and Speech Recognition

Samonte, Mary Jane C.; Bahia, Renz Jirhel D.; Forlaje, Samantha Bernadine A.; Monte, John Gabriel J. Del; Gonzales, Jecelle Anne J.; Sultan, Mariebeth V.

doi:10.1145/3288155.3288182

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…However, 12 studies out of 24 studies did not address any specific SSD (see figure 9). These studies proposed automated tools for a generalized SSD population and experimented without specifying any particular SSD [8,[12][13][14][15][16][17][18][19][20][21][22].…”

Section: Speech Sound Disorders (Rq1)mentioning

confidence: 99%

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

Deka,

Shrivastava,

Nautiyal

et al. 2022

Preprint

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This paper presents a systematic literature review of published studies on AI-based automated speech therapy tools for persons with speech sound disorders (SSD). The COVID-19 pandemic has initiated the requirement for automated speech therapy tools for persons with SSD making speech therapy accessible and affordable. However, there are no guidelines for designing such automated tools and their required degree of automation compared to human experts. In this systematic review, we followed the PRISMA framework to address four research questions: 1) what types of SSD do AI-based automated speech therapy tools address, 2) what is the level of autonomy achieved by such tools, 3) what are the different modes of intervention, and 4) how effective are such tools in comparison with human experts. An extensive search was conducted on digital libraries to find research papers relevant to our study from 2007 to 2022. The results show that AI-based automated speech therapy tools for persons with SSD are increasingly gaining attention among researchers. Articulation disorders were the most frequently addressed SSD based on the reviewed papers. Further, our analysis shows that most researchers proposed fully automated tools without considering the role of other stakeholders. Our

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Section: Speech Sound Disorders (Rq1)mentioning

confidence: 99%

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

Deka,

Shrivastava,

Nautiyal

et al. 2022

Preprint

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show abstract

“…With the evolution of mobile device technology, there has been an increase in the development and adoption of character recognition on the platform in the numerous genres of the app, including within the educational field [5] [6]. According to [7], children tend to be interested in an application that offers both educational aspects and entertainment, therefore, the integration of engaging content and the advantages of smartphone features, tends to motivate the user to engage with the app, enhancing its perception as a productive tool in the process of learning about a specific subject [7] [8].…”

Section: Introductionmentioning

confidence: 99%

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

Harjoseputro

Handarkho

Adie

2019

Int. J. Interact. Mob. Technol.

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<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

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Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems

Bulatov

Arlazarov

2021

Document Analysis and Recognition – ICDAR 2021

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Assistive Mobile App for Children with Hearing & Speech Impairment Using Character and Speech Recognition

Cited by 4 publications

References 17 publications

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems

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