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Figure 3. Logoped 1.0 program-Modern Tools in Patient-Centred Speech Therapy for Romanian Language

The program was designed for the therapy of logoneurosis, while being equally useful for the therapeutic activities used in the treatment of dyslexic-dysgraphic disorders. Logoped 1.0 provides a vast lexical material, which is organised into several sections: exercises involving reading the syllables and the words, sentences reading, followed by phrase and text reading. The colourful design of the words and sentences, the attractive way in which they are displayed on the monitor, and the fact that it allows choosing the exercises level of difficulty render the reading activity much more attractive for the pupil (Tobolcea, 2001). Its functional schema is presented in figure 3.

Figure 4. The relationship between the functional blocks of the systemModern Tools in Patient-Centred Speech Therapy for Romanian Language

The project’s complexity results from the considerable number of research fields it presupposes: artificial intelligence (pattern recognition, learning expert systems), virtual reality, digital signal processing, digital electronic (VLSI), computer architecture, and psychology (assessment procedures and techniques, therapeutic instructions, validation experimental design). In order to assure an assisted therapy one considers the relationships between six functional blocks: patient, speech therapist, office monitor program, expert system, 3D model and patient monitor program. The information flow of the system is given in Figure 4. There is a close connection between the child, as a patient, and his speech therapist. All the other modules are designed so as to contribute to the therapeutic action of the teacher. The monitor program allows realising a complex assessment and collecting information about the child; equally, it provides the opportunity to periodically track the child’s therapy results. The child is provided instant audio feedback which allows him/her to check the audio recordings history. The home monitor program is designed so as to build a virtual interface between teacher and child in order to allow the patient to continue his therapy at home. This component is designed both for the personal computer, which is placed in the therapist’s office, and personal digital assistant (PDA) which is used at home for independent work.

Figure 9. A model that helps the diagnosis prediction-Modern Tools in Patient-Centred Speech Therapy for Romanian Language

We have already implemented many modules of Logo-DM, such as: data cleaning module, data transformation module, feature extraction module, data clustering module and a classification module for diagnosis prediction.  Figure 9 shows the model achieved using a decision tree built on complex examination data that aims to predict the patient’s diagnosis. Currently, we are testing the built models on new cases in order to estimate their quality.

Figure 8. The end-to-end operations in Logo-DM-Modern Tools in Patient-Centred Speech Therapy for Romanian Language

The useful data mining tasks for speech therapy fall into three categories: classification, clustering, and association rules. Classification places children with different speech impairments in predefined classes, and makes possible to track the characteristics of various groups. To model different classes we use many predictor variables (e.g. personal or familial anamnesis data or related to lifestyle). By clustering we group people with speech disorders on the basis of similarity of different features. This helps therapists to understand their patients. Clustering aims to find subsets of a predetermined segment, with homogeneous behavior towards various methods of therapy that can be effectively targeted by a specific therapy, but it is not based on the previous definition of groups (Danubianu, Tobolcea, & Pentiuc, 2009). Association rules aim to find out relationships between different data which seem to have no semantic dependence. The built patterns might be very useful to determine why a specific therapy program has been successful on a segment of patients with speech disorders, and on the other was ineffective. The Logo-DM system was designed to help the speech therapists to optimize the personalized therapy of dyslalia. To understand what kind of knowledge we could discover in TERAPERS’ dataset to improve speech therapy, we have to describe the collected data.

Figure .5 Architecture of the Terapers system-Modern Tools in Patient-Centred Speech Therapy for Romanian Language

Shown in Figure 5, the architecture of the Terapers system implies the existence of two main connected components: on the one hand, an intelligent system which is installed on the office computer of each speech therapist and, on the other, a mobile system which is used as a virtual friend in the therapy applied to the child (Danubianu et al., 2008). The intelligent system – which represents the fixed component of the system – is installed on each computer from the office of the speech therapist; it is made up of the following parts: • an information management module for children; • an expert system, able to produce inferences based on the data given by the assessment module; • a mouth virtual module which allows the display of all hidden movements that are likely to occur during speech; • a management module of the exercises uses, which allows creating or modifying the exercises, depending on the various therapy stages, as well as their organization into complex issues.

closedIPD-NOFeb 2026View details →

Clinical Feasibility & Validation of the Virtual Reality GlenxRose Speech-Language Therapies

ClinicalTrials.gov study NCT05695131. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

A Trial Investigating Telerehabilitation as an add-on to Face-to-face Speech and Language Therapy in Post-stroke Aphasia.

ClinicalTrials.gov study NCT03228264. IPD Sharing: NO. Countries: 1. Publications: 11.

Timing of Transcranial Direct Current Stimulation (tDCS) Combined With Speech and Language Therapy (SLT)

ClinicalTrials.gov study NCT03773406. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

Speech and Language Therapy After Stroke

ClinicalTrials.gov study NCT00713050. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Memantine and Intensive Speech-Language Therapy in Aphasia

ClinicalTrials.gov study NCT00640198. IPD Sharing: Not stated. Countries: 1. Publications: 3.

ClinicalTrials.gov24/100

Comparative Effects of Play Based Therapy and Functional Communication Training in Speech-language Delayed Children

ClinicalTrials.gov study NCT05749315. IPD Sharing: Not stated. Countries: 1. Publications: 0.

ClinicalTrials.gov20/100

The Effect of Transcranial Direct Current Stimulation and Speech Language Therapy to Improve Language Functioning in Arabic Speakers With Aphasia Post-Stroke

ClinicalTrials.gov study NCT04644718. IPD Sharing: Not stated. Countries: 0. Publications: 0.