SPELTA: An expert system to generate therapy plans for speech and language disorders

Robles-Bykbaev, Vladimir; López‐Nores, Martín; Pazos‐Arias, José J.; Arévalo-Lucero, Daysi

doi:10.1016/j.eswa.2015.06.011

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…First-level clusters represent groups of patients who may have similar speech-language skills and limitations, but possibly arising from (or linked to) different medical conditions. To create these groups, we use the distance metrics of Figure 2 , where S i and S j refer to two different subjects, A is one of the SL areas, f goes over the set of features from the medical records relevant for that area ( features MR (A) ), and ManhDist denotes the mean-Manhattan binary distance [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…The first summation measures the mean-Manhattan binary distance of the initial SLT evaluations of two subjects, considering only the dimensions relevant to the speech-language area in question, dimensions IE (A) . The second summation provides a scale factor derived from the absolute differences of cognitive age, gap in language development, expressive language age, and receptive language age (the features of cognitive development data) [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…There have been several approaches to automate diagnostic tests by means of audiovisual signal processing [ 12 - 15 ] and to automate the generation of therapy plans for specific disorders [ 16 , 17 ]. In this paper, we evaluate the support provided to SLPs by the SPELTA (SPEech and Language Therapy Assistant) expert system presented by Robles-Bykbaev et al [ 18 ], which aims to automatically generate therapy plans for SLT, containing semiannual activities and daily exercises for an unrestricted range of disorders affecting the five areas of hearing, oral structure and function, linguistic formulation, expressive language and articulation, and receptive language. The goal is to assess whether the expert system can speed up the SLPs’ work and lead to more accurate, consistent, and complete therapy plans for their patients.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of an Expert System for the Generation of Speech and Language Therapy Plans

Robles-Bykbaev¹,

López‐Nores²,

García‐Duque³

et al. 2016

JMIR Med Inform

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BackgroundSpeech and language pathologists (SLPs) deal with a wide spectrum of disorders, arising from many different conditions, that affect voice, speech, language, and swallowing capabilities in different ways. Therefore, the outcomes of Speech and Language Therapy (SLT) are highly dependent on the accurate, consistent, and complete design of personalized therapy plans. However, SLPs often have very limited time to work with their patients and to browse the large (and growing) catalogue of activities and specific exercises that can be put into therapy plans. As a consequence, many plans are suboptimal and fail to address the specific needs of each patient.ObjectiveWe aimed to evaluate an expert system that automatically generates plans for speech and language therapy, containing semiannual activities in the five areas of hearing, oral structure and function, linguistic formulation, expressive language and articulation, and receptive language. The goal was to assess whether the expert system speeds up the SLPs’ work and leads to more accurate, consistent, and complete therapy plans for their patients.MethodsWe examined the evaluation results of the SPELTA expert system in supporting the decision making of 4 SLPs treating children in three special education institutions in Ecuador. The expert system was first trained with data from 117 cases, including medical data; diagnosis for voice, speech, language and swallowing capabilities; and therapy plans created manually by the SLPs. It was then used to automatically generate new therapy plans for 13 new patients. The SLPs were finally asked to evaluate the accuracy, consistency, and completeness of those plans. A four-fold cross-validation experiment was also run on the original corpus of 117 cases in order to assess the significance of the results.ResultsThe evaluation showed that 87% of the outputs provided by the SPELTA expert system were considered valid therapy plans for the different areas. The SLPs rated the overall accuracy, consistency, and completeness of the proposed activities with 4.65, 4.6, and 4.6 points (to a maximum of 5), respectively. The ratings for the subplans generated for the areas of hearing, oral structure and function, and linguistic formulation were nearly perfect, whereas the subplans for expressive language and articulation and for receptive language failed to deal properly with some of the subject cases. Overall, the SLPs indicated that over 90% of the subplans generated automatically were “better than” or “as good as” what the SLPs would have created manually if given the average time they can devote to the task. The cross-validation experiment yielded very similar results.ConclusionsThe results show that the SPELTA expert system provides valuable input for SLPs to design proper therapy plans for their patients, in a shorter time and considering a larger set of activities than proceeding manually. The algorithms worked well even in the presence of a sparse corpus, and the evidence suggests that the system will become more reliable as ...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of an Expert System for the Generation of Speech and Language Therapy Plans

Robles-Bykbaev¹,

López‐Nores²,

García‐Duque³

et al. 2016

JMIR Med Inform

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“…This ability was considered by experts to develop as a therapeutic aid for speech and language disorders. This consideration is based on research which is conducted by Robles-Bykbaev, et al in 2015 [24]. Its hope, by using the stochastic model can improve the performance of AASC.…”

Section: Future Workmentioning

confidence: 99%

Voice Biometric System: The Identification of the Severity of Cerebral Palsy using Mel-Frequencies Stochastics Approach

Nafisah

Effendy

2019

IJIE

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Cerebral Palsy (CP) is known as one type of disabilities that affects children. CP is an 'umbrella' for all chronic neurologic disorders which was manifested in movement control disorders. They arise early in life, commonly initiated with non-progressive diseases [1]. This neurological condition causes problems in body movement and muscle control that can inhibit the development of children's speech. The inhibition of speech development in CP is caused by the stiffness of the muscles around the mouth and face. It causes the children with CP having difficulty coordinating their speech organs. In CP, there is a disorder occurs that causes the muscles to move on their own without realizing it. This disorder is called dystonia. Regarding the development of speech on CP where the development is affected by the stiffness of the muscles around the mouth, then dystonia can be an indication of the severity level of CP. Based on dystonia that occurs on CP, CP can be classified into 4 severity levels: Slight, Mild, Moderate, and Severe. In Slight level, dystonia occurs less than 10% of the time and doesn't interfere with speech. Whereas in mild level of severity, dystonia occurs less than 50% and doesn't interfere with the speech. In moderate level, speech impairments caused by dystonia in CP begin to suffer where the dystonia is more than 50% of the time. Even at severe level, children with CP are difficult to move his/her mouth [2],[3]. Dystonia that occurs in CP causes CP speech has unique characteristics like "slurred," "choppy," or "mumbled" that may be difficult to understand. CP speech is also at a slow rate and the rapid rate in mumbling quality. Voice quality changes in CP, such as hoarse and breathy voice or speech that sounds "nasal" or "stuffy", are caused by limited tongue,

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“…Using this information a SLP is able to determine the patient's skills in each of the five areas mentioned above. Likewise, the expert system can generate therapy plans containing general guidelines to conduct activities for long-term periods (6 months) [22]. Each therapy plan consists of an ensemble of sub plans in each of the five areas previously mentioned and is generated using a multilevel cluster approach based on Partition Around Medoids and K Nearest Neighbors criterion.…”

Section: Proposed Ecosystemmentioning

confidence: 99%

A Proposal of an Ecosystem Based on Intelligent Ict Tools to Support the Diagnosis and Intervention of Patients With Communication Disorders

López‐Nores¹,

García‐Duque²,

Pazos‐Arias³

et al. 2016

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SPELTA: An expert system to generate therapy plans for speech and language disorders

Cited by 22 publications

References 23 publications

Evaluation of an Expert System for the Generation of Speech and Language Therapy Plans

Evaluation of an Expert System for the Generation of Speech and Language Therapy Plans

Voice Biometric System: The Identification of the Severity of Cerebral Palsy using Mel-Frequencies Stochastics Approach

A Proposal of an Ecosystem Based on Intelligent Ict Tools to Support the Diagnosis and Intervention of Patients With Communication Disorders

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