Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia

Purpose Individuals with neurogenic speech disorders require ongoing therapeutic support to achieve functional communication goals. Alternative methods for service delivery, such as tablet-based speech therapy applications, may help bridge the gap and bring therapeutic interventions to the patient in an engaging way. The purpose of this study was to evaluate an iPad-based speech therapy app that uses automatic speech recognition (ASR) software to provide feedback on speech accuracy to determine the ASR's accuracy against human judgment and whether participants' speech improved with this ASR-based feedback. Method Five participants with apraxia of speech plus aphasia secondary to stroke completed an intensive 4-week at-home therapy program using a novel word training app with built-in ASR. Multiple baselines across participants and behaviors designs were employed, with weekly probes and follow-up at 1 month posttreatment. Four sessions a week of 100 practice trials each were prescribed, with 1 being clinician-run and the remainder done independently. Dependent variables of interest were ASR–human agreement on accuracy during practice trials and human-judged word production accuracy over time in probes. Also, user experience surveys were completed immediately posttreatment. Results ASR–human agreement on accuracy averaged ~80%, which is a common threshold applied for interrater agreement. All participants demonstrated improved word production accuracy over time with the ASR-based feedback and maintenance of gains after 1 month. All participants reported enjoying using the app with support of a speech pathologist. Conclusion For these participants with apraxia of speech plus aphasia due to stroke, satisfactory gains were made in word production accuracy with an app-based therapy program providing ASR-based feedback on accuracy. Findings support further testing of this ASR-based approach as a supplement to clinician-run sessions to assist clients with similar profiles in achieving higher amount and intensity of practice as well as empowering them to manage their own therapy program. Supplemental Material https://doi.org/10.23641/asha.8206628

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A neurophonetic perspective on articulation planning

Proceedings of 11th International Conference of Experimental Linguistics ◽

10.36505/exling-2020/11/0003/000418 ◽

2020 ◽

Author(s):

Wolfram Ziegler

Keyword(s):

Motor Learning ◽

Higher Order ◽

Speech Development ◽

Word Production ◽

Apraxia Of Speech ◽

Clinical Model ◽

Motor Processes ◽

Speech Motor Learning ◽

Speech Motor ◽

Production Accuracy

This paper gives an overview of a model that predicts articulation ease for German phonological words on the basis of error data from patients with apraxia of speech (AOS). AOS is introduced as a clinical model of higher order motor processes for articulation. Word production accuracy in AOS is considered as a window into the structure of articulation plans as acquired through speech motor learning in childhood. The NLG model of apraxia of speech is explained. Applications in speech development and adult speech are outlined.

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Literacy by Way of Automatic Speech Recognition

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch121 ◽

2011 ◽

pp. 2074-2118

Author(s):

Russell Gluck ◽

John Fulcher

Keyword(s):

Neural Networks ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Markov Models ◽

Hidden Markov ◽

Time Warping ◽

Oral Storytelling ◽

Pattern Recognition Techniques ◽

Dynamic Time ◽

Over Time

The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov models (HMMs), but also the tried-and-proven techniques of dynamic time warping and artificial neural networks (ANNs). The coverage then switches to Gluck’s (2004) draw-talk-write (DTW) process, developed over the past two decades to assist non-text literate people become gradually literate over time through telling and/or drawing their own stories. DTW has proved especially effective with “illiterate” people from strong oral, storytelling traditions. The chapter concludes by relating attempts to date in automating the DTW process using ANN-based pattern recognition techniques on an Apple Macintosh G4™ platform.

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Evaluating Automatic Speech Recognition for Child Speech Therapy Applications

The 21st International ACM SIGACCESS Conference on Computers and Accessibility - ASSETS '19 ◽

10.1145/3308561.3354606 ◽

2019 ◽

Cited By ~ 1

Author(s):

Adam Hair ◽

Kirrie J. Ballard ◽

Beena Ahmed ◽

Ricardo Gutierrez-Osuna

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Speech Therapy ◽

Child Speech

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Speech Therapy for Childhood Apraxia of Speech

Ramathibodi Medical Journal ◽

10.33165/rmj.2019.42.1.113728 ◽

2019 ◽

Vol 42 (1) ◽

pp. 55-64

Author(s):

Worawan Wattanawongsawang

Keyword(s):

Speech Therapy ◽

Daily Life ◽

Speech Sound ◽

Motor Planning ◽

Apraxia Of Speech ◽

Speech Sound Disorder ◽

Childhood Apraxia Of Speech ◽

Therapy Program ◽

Team Working ◽

The Family

Childhood apraxia of speech is a neurological speech sound disorder in which the child has inadequate the precision and consistency of movements underlying speech production in the absence of neuromuscular deficits. Children with apraxia of speech require intensive and specialized training in order to enable them to communicate effectively. The principles of the speech therapy program include stimulating speaking and communicating in daily life as well as practicing to speak clearly. The purpose of this article is to discuss the principles of speech therapy based on motor learning, speech stimulation and daily life communication, exercises to promote oral motor planning for each speech sound, and inclusion of the family into the team working with the child.

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