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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence: 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Virtual Event, September 7–9, 2021, Proceedings
EditorsGoreti Marreiros, Francisco S. Melo, Nuno Lau, Henrique Lopes Cardoso, Luís Paulo Reis
Place of PublicationCham
PublisherSpringer
Pages596-608
Number of pages13
ISBN (Electronic)978-3-030-86230-5
ISBN (Print)978-3-030-86229-9
DOIs
Publication statusPublished - 2021
Event20th EPIA Conference on Artificial Intelligence, EPIA 2021 - Virtual, Online
Duration: 7 Sep 20219 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12981 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th EPIA Conference on Artificial Intelligence, EPIA 2021
CityVirtual, Online
Period7/09/219/09/21

Keywords

  • Deep learning
  • Model deployment
  • Sibilant consonants
  • Speech and language therapy

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