Ovidiu Andrei Schipor

Distributed Intelligent System for Personalized Therapy of Speech Disorders

The aim of this paper is to describe an intelligent system designed for assisting the personalized therapy of dyslalia for the Romanian prescholars children. This system is developed in the frame of TERAPERS project that can be included in a very important research area: informational technologies in response to society challenges - for health: early diagnosis, personalized therapy. Taking into consideration the fact that Romanian language is a phonetic one that has its own special linguistic particularities, there is a real need for the development and use of audio-video systems, which can be used in the therapy of different speech problems. The system has a high degree of originality because his objective is to treat the pronunciation disorders in Romanian language. Also the complexity of the project results from the high number of different research areas involved: artificial intelligence, virtual reality, digital signal processing, digital electronic and psychology.

Towards a multimodal emotion recognition framework to be integrated in a Computer Based Speech Therapy System

Emotion recognition has become a “must have” for all system that want to inspire users confidence and to interact in a friendly and familiar way. In this paper we propose an improved CBST (Computer Based Speech Therapy System) architecture by using multimodal (i.e. paralanguage, visual, and physiological parameters) emotion recognition techniques. Most research on emotion recognition using speech analysis so far has focused on adult subjects, with a good pronunciation. However, little research has been conducted on adapting classical affect recognition techniques in “narrow areas” such as children speech therapy, where emotions play a key role. So, our paper aims to deal with the assessment of the affective state of the children with speech disorders. A brief literature review is presented, exploring the recent work in the area. New hypothesis are formulated in order to identify the limits of using classical emotion recognition techniques in this special conditions. An original framework to be integrated in the CBST architecture is also outlined. The proposed framework can be seen as an extension of a CBST but will be flexible to other learning systems too.

Using a Fuzzy Emotion Model in Computer Assisted Speech Therapy

Affective computing – machine’s ability to recognize and simulate human affects – has become a main research field for Human Computer Interaction. This paper deal with emotion recognition within a CBST (Computer Based Speech Therapy System) for preschoolers and young schoolchildren. Identifying the emotions of children with speech disorders during the assisted therapy sessions requires an adaptation of classical recognition techniques. That is why, in our article we focus on finding and testing the best emotion representation model to be used in this narrow field. An experiment that validates our proposed approach and indicates the probabilistic coefficient matrix is also presented. The proposed emotion recognition framework can be seen as a future extension of our CBST – Logomon.

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 pronunci‐ ation, which can be individualized based on specialty needs, previous experiences, and the childs 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.