Two-way English-Filipino/Bisaya/Ilocano web-capable translator for the personal digital assistant
In this time of increasing globalization, understanding languages are essential in cross-cultural communication. In the Philippines, with its 7,100 islands, the problem of translation and language differences between non-natives and natives is heightened with the existence of approximately 76 major...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Animo Repository
2006
|
Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/etd_honors/247 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | De La Salle University |
Language: | English |
Summary: | In this time of increasing globalization, understanding languages are essential in cross-cultural communication. In the Philippines, with its 7,100 islands, the problem of translation and language differences between non-natives and natives is heightened with the existence of approximately 76 major language groups and more than 500 dialects. With that many languages present, human translators versed in different Philippine dialects are few and quite expensive to employ. Automation of translation, in the form of machine translation, addresses the need for fast and efficient translation.
This study provides a translation system implemented in a Personal Digital Assistant, capable of converting words, sentences and phrases from English to Filipino, English to Bisaya, English to Ilocano and vice versa. The system is able to accept audio or textual input, and processes this to return a textual translation. Audio output is optional and limited. This system is designed to work with a web server for the optional audio output.
Various surveys and tests have proven that the system produces translated output of intelligible and accurate sentences. Survey tests show that the output of the system is at least 82.8% intelligible and 76.6% accurate. Functional tests show that word translations are at least 86.67% accurate, while sentence translations are at least 70% accurate. |
---|