Design and Development of a Smart Voice Reminder Device

Citation

Oh, Tick Hui and Pang, Sheen Ye and Lee, Jacob Daryl Yuon Ee (2024) Design and Development of a Smart Voice Reminder Device. International Journal of Integrated Engineering (IJIE), 16 (3). pp. 45-54. ISSN 2229-838X, 2600-7916

[img] Text
View of Design and Development of a Smart Voice Reminder Device.pdf - Published Version
Restricted to Repository staff only

Download (2MB)

Abstract

During the COVID-19 pandemic period, an average student receives more assignments from their teachers while taking online classes. It can be challenging for these young minds to keep track of every assignment’s deadline on top of the various online classes that require their attendance and punctuality. So, to prevent thestudents from missing any of their online classes and assignment submission, a solution more than just a calendar’s reminder is required as many primary school students might not have access to smartphones. Hence, a voice reminder device is proposed in this paper for this purpose. Raspberry Pi 4 is used to build this device, along with a Python-based program to connect it to the Google calendar server. The system's speech recognition is done using a Python speech recognition package. The system uses the Google Cloud Speech API to translate the user's speech input into a text format that the system can understand. The information required to produce the user-specified event is then extracted along with the activation word, all from the user's text. The system then saves the event into Google calendar, using the Google Credentials.json file as its destination file after extracting all the important data. The user can add, edit, or delete any previously established event using the Google calendar from any location as long as the device is online. It can also generate a timetable through voice command. Other notable usage of this device includes keeping elderlies with Alzheimer's disease on track with their important tasks like scheduled medication and medical follow-ups. The prototype managed to achieve a rather high accuracy rate of 85.6% through its speech recognition function.

Item Type: Article
Uncontrolled Keywords: Speech recognition systems
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 May 2024 02:52
Last Modified: 31 May 2024 02:52
URII: http://shdl.mmu.edu.my/id/eprint/12509

Downloads

Downloads per month over past year

View ItemEdit (login required)