Deploying a Speech Therapy Game Using a Deep Neural Network Sibilant Consonants Classifier

Speech therapy games present a relevant application of business intelligence to real-world problems. However many such models are only studied in a research environment and lack the discussion on the practical issues related to their deployment. In this article, we depict the main aspects that are critical to the deployment of a real-time sound recognition neural model. We have previously presented a classifier of a serious game for mobile platforms that allows children to practice their isolated sibilants exercises at home to correct sibilant distortions, which was further motivated by the Covid-19 pandemic present at the time this article is posted. Since the current classifier reached an accuracy of over 95%, we conducted a study on the ongoing issues for deploying the game. Such issues include pruning and optimization of the current classifier to ensure near real-time classifications and silence detection to prevent sending silence segment requests to the classifier. To analyze if the classification is done in a tolerable amount of time, several requests were done to the server with pre-defined time intervals and the interval of time between the request and response was recorded. Deploying a program presents new obstacles, from choosing host providers to ensuring everything runs smoothly and on time. This paper proposes a guide to deploying an application containing a neural network classifier to free- and controlled-cost cloud servers to motivate further deployment research.