From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy

This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computer‐based speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for real‐life situations. We present data from 1‐year application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child\u27s prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use “self‐learning” capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc

The Terminology Use and Diagnostic Approaches of Paediatric Speech and Language Therapists in the UK

In recent years, the field of speech and language therapy has highly critiqued the existence of a vast number of diagnostic terms, all referring to paediatric language diagnoses. Substantive scholarly discussion has addressed the negative consequences of terminological inconsistency, including poor public awareness, underfunding of research and compromised lobbying efforts. Whilst many terms are used across research literature, there is no empirical evidence to show the diagnostic terminology used by speech and language therapists (SLTs) in clinical contexts. Although speculated, it is also unclear as to why variation may exist and persist, despite efforts to resolve it. The current study takes a mixed methods approach, supported by dialectical critical realism, to identify the terms used to denote speech, language and fluency diagnoses by SLTs in the UK. It also examines the underpinning reasons and investigates the clinical diagnostic process. A survey of SLTs (n=374) revealed which terms are commonly used in UK clinical practice. Associations between the use of terms and eight clinician-related factors (e.g. geographical location and workplace characteristics) were measured using chi-square testing. Semi-structured interviews sought the perspectives of 22 SLTs regarding their terminology use and diagnostic processes. The summation of both stages indicate that terminological consistency is a shared goal across the profession, but the nature of clinical practice poses substantial challenges to this. Interviewees reported taking an individualised approach to diagnosis with each client and adapting terms to meet the needs of families; however, this is not compatible with overall consistency. Participants disclosed barriers in their diagnostic practice such as limited clinical time, low public understanding of diagnostic terminology and the challenges of working with the highly varied needs of families. The confidence of SLTs to make diagnoses was generally low, resulting in alternative strategies to diagnosis being employed -such as the use of descriptions and colloquial terms. The impact and potential disparities associated with individualising terminology are considered and recommendations are made to support clinicians in their diagnostic practice