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Title |
Smart Time-Based Reminder System Using Speech Recognition Supported by Arabic Language |
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Author |
Fahad Alotaibi and Nashwan Alromema |
| Citation |
Vol. 22 No. 11 pp. 391-398 |
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Abstract |
In the present world, people have to schedule a number of tasks to be accomplished in advance. These tasks vary from the meetings at work to the grocery shopping. Since it is hard to remember all the activities, the utilization of reminder systems on smart phones has come into place. The time-based reminders are common in the traditional methods, which help to alert users to the particular date and time. The available reminder application is based on written texts. Most of them do not support location-based reminding where the user is notified and the alarm triggers depending on arriving or leaving a specific place. To our knowledge, all available automatic reminders based on speech do not support Arabic language. This research work focuses on having a smart time-based system that allows the user to add a reminder in a modern, easy, and smooth way. The user can create a speech reminder where the proposed application can convert speech into text and makes the necessary processing to extract required information such as the title, time, place, and description. The proposed application can be set to send the notification based on a specific location either when arriving or leaving that place. The application will schedule the reminder appropriately and easily allowing users to manage their daily tasks quickly and sharing the reminder with several users |
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Keywords |
Markov Model, speech recognition, reminder, Mobile-App, SDLC, WFM. |
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