Speech and Language Therapy Technologies for Inclusive Educational Practices

Abstract

The speech and language therapy technologies are increasingly vital in supporting the inclusion of the practice to learners with communicational disorder. This research paper talks about the limitations of traditional interventions that are therapist-dependent and are characterized by inability to scale, lagging feedback and poor personalization. It suggests a hybrid algorithm of multimodal speech, phoneme and articulation assessment with the Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The system is a combination of personalization (through reinforcement learning), feedback loops, and real-time audio processing. Evaluation using experimental data on school data in experimental data indicates a 92.6 accuracy rate, as opposed to 81.4 with traditional rule-based systems. The rate of pronunciation error detection was also enhanced by 38.2 and therapy response time decreased by 46.7. The level of student engagement improved to 85.1% as opposed to 52.3% thus showing great improvement in inclusivity. It is based on multilingual flexibility and low-resource implementation, allowing more users to gain access within a variety of learning settings. Findings are that there are better learning outcomes, regular monitoring, and the ability to deliver interventions on a large scale. Its areas of integration are with the classroom platforms, assistive devices and policy-based inclusive education systems. This paper concludes that AI-based speech therapy technologies can contribute greatly to the field of accessibility, efficiency and personalization, creating a strong avenue towards inclusion and equitable learning settings. Comparative evaluation with control machine learning models like Support Vector Machines and Random Forest shows a steady high performance in terms of accuracy, recall and F1-score that have had an average improvement of 12.8, 14.3 and 13.6 respectively. The suggested solution will provide educators and therapists with a data-based intervention planning and constant progress monitoring to minimize the workload and enhance the accuracy of decisions in the classroom setting that involves special needs students.