Development of a Remote Therapy Tool for Childhood Apraxia of Speech

Parnandi, Avinash; Karappa, Virendra; Lan, Tian; Shahin, Mostafa; McKechnie, Jacqueline; Ballard, Kirrie J.; Ahmed, Beena; Gutierrez‐Osuna, Ricardo

doi:10.1145/2776895

Cited by 23 publications

(15 citation statements)

References 28 publications

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“…The system uses the visual animation of an animated agent to deliver speech and language therapy to persons with aphasia. Other systems allow a speech language pathologist to assign speech production exercises to each child through a web interface [42]. Then, the child practices these exercises in the mobile app and the pathologist can review the individual recordings and the automated scores assigned by the system through a web interface.…”

Section: A Speech Technologies For Pronunciation Trainingmentioning

confidence: 99%

Evaluating the Impact of an Autonomous Playing Mode in a Learning Game to Train Oral Skills of Users With Down Syndrome

et al. 2021

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The use of ICT tools is broadly extended among people with intellectual disabilities and also, to a lesser degree, the use of learning tools including learning games. Although the use of learning games is widely accepted due to its high engagement capacity, there are few studies that analyze its usability for people with intellectual disabilities. This work presents an evaluation of the impact of adding an autonomous playing mode on the usability of a learning game designed to aid the training of oral skills in people with Down syndrome. A study in which the effectiveness, efficiency and user satisfaction of a learning game are compared when the system is used with different degrees of teacher supervision is carried out. A learning game originally designed to train oral competencies for people with Down syndrome in a teacher supervised scenario is adapted to allow its autonomous use, by including a module that provides the automatic assessment of oral productions . The use of the tool is thus compared in three different scenarios: a supervised environment, autonomous use, and laboratory use with multiple users working in parallel. The different usability evaluation instruments used reveal that, although there are no differences in the degree of engagement, there may be important differences regarding session performance: the quality of the audios is lower in the laboratory sessions and the number of errors increases in the autonomous sessions. We conclude that, although the autonomous use of learning games by users with intellectual disabilities is possible, and this can lead to considerable savings in human resources, if the feedback provided by the game is not comparable with that provided by the teacher, performance may drop considerably although the degree of engagement is maintained.

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Section: A Speech Technologies For Pronunciation Trainingmentioning

confidence: 99%

Evaluating the Impact of an Autonomous Playing Mode in a Learning Game to Train Oral Skills of Users With Down Syndrome

et al. 2021

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“…Text entry [9,38,67,71,82,85,93] Application control [22,90,92] Speech analysis [26,36,52,68,72] Spoken output [28,31,66,77] Conversational Agents [33,43,45,48,55,61,96] Probes [18,74] Domains Education [22,43,52,61,68,77] Health & Well-being [26,28,36,71,72,92,94] Transportation [33,38,55,67,90] Gaming [18,26,36,52,66,…”

Section: References Themesmentioning

confidence: 99%

“…Parnandi et al [72] also created an application that required the users to produce utterance in response to images displayed on the screen, this time with a focus on speech therapy for children with apraxia of speech. In this case, the system was able to identify insertion, deletion, and substitution mispronunciations on a phoneme by phoneme level.…”

Section: Speech Analysismentioning

confidence: 99%

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Conversational User Interfaces on Mobile Devices

Jaber

2020

Proceedings of the 2nd Conference on Conversational User Interfaces

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Conversational User Interfaces (CUI) on mobile devices are the most accessible and widespread examples of voice-based interaction in the wild. This paper presents a survey of mobile conversation user interface research since the commercial deployment of Apple's Siri, the first readily available consumer CUI. We present and discuss Text Entry & Typing, Application Control, Speech Analysis, Conversational Agents, Spoken Output, & Probes as the prevalent themes of research in this area. We also discuss this body of work in relation to the domains of Health & Well-being, Education, Games, and Transportation. We conclude this paper with a discussion on Multi-modal CUIs, Conversational Repair, and the implications for CUIs of greater access to the context of use. CCS CONCEPTS• Computing methodologies → Speech recognition; • Humancentered computing → Interaction techniques; Smartphones; • General and reference → Surveys and overviews.

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“…Investigators may also seek to use ML therapeutically, as with performance assessments. For example, investigators can identify and count classes of behavioral output in training contexts, such as the number of words uttered during speech therapy (54) or the number of functional primitives performed during occupational therapy (12).…”

Section: Machine Learning For Other Clinical Applicationmentioning

confidence: 99%

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

et al. 2019

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Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.

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Development of a Remote Therapy Tool for Childhood Apraxia of Speech

Cited by 23 publications

References 28 publications

Evaluating the Impact of an Autonomous Playing Mode in a Learning Game to Train Oral Skills of Users With Down Syndrome

Evaluating the Impact of an Autonomous Playing Mode in a Learning Game to Train Oral Skills of Users With Down Syndrome

Conversational User Interfaces on Mobile Devices

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

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