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

Abstract Speech and Language Pathologists have to treat a wide spectrum of disorders, working with a large set of exercises and activities to design personalized therapy plans for their patients. This paper presents an expert system designed to provide support in that labor by automatically generating therapy plans containing semiannual activities in the areas of hearing, oral structure and function, linguistic formulation, expressive language + articulation, and receptive language. The system relies on an implementation of the Partition Around Medoids (PAM) algorithm to generate clusters of subject profiles with two levels of granularity, first considering broad diagnosis terms and medical conditions, and then looking at the specific communication skills affected. The proposal has been tested in collaboration with expert pathologists from three special education institutions of Ecuador, who were about 90% satisfied with the quality of the therapy plans provided. It was found that the two-level clustering is a crucial feature to tell apart individuals who have similar speech–language limitations, but arising from different medical conditions and, therefore, requiring different treatment.

[1]  Theodoros Kostoulas,et al.  Affective speech interface in serious games for supporting therapy of mental disorders , 2012, Expert Syst. Appl..

[2]  Sazali Yaacob,et al.  Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques , 2012, Journal of Medical Systems.

[3]  Charles R Doarn,et al.  Overview of telehealth activities in speech-language pathology. , 2008, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[4]  Oscar Saz-Torralba,et al.  Tools and Technologies for Computer-Aided Speech and Language Therapy , 2009, Speech Commun..

[5]  Yiannis Kompatsiaris,et al.  Two-level Message Clustering for Topic Detection in Twitter , 2014, SNOW-DC@WWW.

[6]  Felipe Trujillo-Romero,et al.  Evolutionary approach for integration of multiple pronunciation patterns for enhancement of dysarthric speech recognition , 2014, Expert Syst. Appl..

[7]  Hatice Kose-Bagci,et al.  Socially Interactive Robotic Platforms as Sign Language Tutors , 2014, Int. J. Humanoid Robotics.

[8]  Roderic A. Grupen,et al.  Multidisciplinary stroke rehabilitation delivered by a humanoid robot: Interaction between speech and physical therapies , 2013 .

[9]  Tommy W. S. Chow,et al.  A multi-level matching method with hybrid similarity for document retrieval , 2012, Expert Syst. Appl..

[10]  Siti Salwah Salim,et al.  Exploring the influence of general and specific factors on the recognition accuracy of an ASR system for dysarthric speaker , 2015, Expert Syst. Appl..

[11]  Jorge García Duque,et al.  Modelling Domain Knowledge of Speech and Language Therapy with an OWL Ontology and OpenEHR Archetypes , 2015, HEALTHINF.

[12]  Maciej Haranczyk,et al.  Comparison of Nonbinary Similarity Coefficients for Similarity Searching, Clustering and Compound Selection , 2009, J. Chem. Inf. Model..

[13]  Nadine Martin,et al.  Virtual clinicians for the treatment of aphasia and speech disorders , 2013, 2013 International Conference on Virtual Rehabilitation (ICVR).

[14]  Mohamed S. Kamel,et al.  An Efficient Two-Level SOMART Document Clustering Through Dimensionality Reduction , 2004, ICONIP.

[15]  Rosa Gassió-Subirachs,et al.  Trastornos del lenguaje , 2006 .

[16]  Stefan Gheorghe Pentiuc,et al.  Automatic Assessment of Pronunciation Quality of Children within Assisted Speech Therapy , 2012 .

[17]  J. Marshall,et al.  Speech and language therapy to improve the communication skills of children with cerebral palsy. , 2004, The Cochrane database of systematic reviews.

[18]  Wolfram Ziegler,et al.  The actual and potential use of gestures for communication in aphasia , 2013 .

[19]  Stefan Gheorghe Pentiuc,et al.  Improving Computer Based Speech Therapy Using a Fuzzy Expert System , 2010, Comput. Informatics.

[20]  Tung-Hsu Hou,et al.  An intelligent model for the classification of children's occupational therapy problems , 2012, Expert Syst. Appl..

[21]  Yoon Phaik Ooi,et al.  Application of a web-based cognitive-behavioural therapy programme for the treatment of selective mutism in Singapore: a case series study. , 2012, Singapore medical journal.

[22]  Kazuki Sekine,et al.  The relationship of aphasia type and gesture production in people with aphasia. , 2013, American journal of speech-language pathology.

[23]  Munyaradzi Dimairo,et al.  , Poststroke : A Pilot Randomized Controlled Trial Computer Therapy Compared With Usual Care for People With Long-Standing Aphasia , 2012 .

[24]  Martin J. Ball,et al.  The Handbook of Language and Speech Disorders , 2010 .

[25]  Rune Matthiesen,et al.  Overview on techniques in cluster analysis. , 2010, Methods in molecular biology.

[26]  Isabel Trancoso,et al.  Automatic word naming recognition for an on-line aphasia treatment system , 2013, Comput. Speech Lang..

[27]  Visar Berisha,et al.  Speech assist: An augmentative tool for practice in speech-language pathology , 2013 .

[28]  Yves Lechevallier,et al.  Multilevel Clustering for Large Databases , 2007 .

[29]  Dinh Q. Phung,et al.  Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts , 2014, ICML.

[30]  Deborah C. Beidel,et al.  Incorporating Technology Into the Treatment of a 17-Year-Old Female With Selective Mutism , 2013 .

[31]  A. Aronson.  Clinical Voice Disorders , 2009 .

[32]  Antanas Verikas,et al.  Questionnaire- versus voice-based screening for laryngeal disorders , 2012, Expert Syst. Appl..