CUCHILD: A Large-Scale Cantonese Corpus of Child Speech for Phonology and Articulation Assessment

Ng, S. Thomas; Ng, Cymie Wing-Yee; Wang, Jiarui; Lee, Tan; Lee, Kathy; Tong, Michael C. F.

doi:10.48550/arxiv.2008.03188

Cited by 1 publication

(2 citation statements)

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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%

“…In another study, Rivas et al proposed using a virtual world to provide speech therapy for children with dyslalia [32]. It is worth noting that only one study was related to speech data collection for Cantonese to perform phonology and articulation assessment [13]. The figure 10 shows the distribution of papers addressing specific SSD.…”

Section: Speech Sound Disorders (Rq1)mentioning

confidence: 99%

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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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