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Duc Chung Tran
FPT University
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
The first FOSD-tacotron-2-based text-to-speech application for Vietnamese Duc Chung Tran
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2539

Abstract

Recently, with the development and deployment of voicebots which help to minimize personnels at call centers, text-to-speech (TTS) systems supporting English and Chinese have attracted attentions of researchers and corporates worldwide. However, there is very limited published works in TTS developed for Vietnamese. Thus, this paper presents in detail the first Tacotron-2-based TTS application development for Vietnamese that utilizes the publicly available FPT open speech dataset (FOSD) containing approximately 30 hours of labeled audio files together with their transcripts. The dataset was made available by FPT Corporation with an open access license. A new cleaner was developed for supporting Vietnamese language rather than English which was provided by default in Mozilla TTS source code. After 225,000 training steps, the generated speeches have mean opinion score (MOS) well above the average value of 2.50 and center around 3.00 for both clearness and naturalness in a crowd-source survey.
An open toolbox for generating map of actively confirmed SARS-CoV-2 or COVID-19 cases in Vietnam Duc Chung Tran
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2621

Abstract

The recent outbreak of novel coronavirus, SARS-CoV-2 or COVID-19, discovered in late 2019, being continued to spread across regions worldwide, has resulted in 1,914,916 “confirmed” cases with up to 123,010 deaths, as in situation report –85 by World Health Organization (WHO). Most of the developed disease monitoring and tracking tools currently available only present the reported cases up to country-level and not detail down to provincial- or state-, city- level within the countries. This is insignificant for supporting activities in quickly reducing and preventing the spread of the disease within a certain country because further detail potential infectious locations are not provided for people to avoid traveling or passing by there. Thus, this work presents an open toolbox for generating map of actively “Confirmed” cases in a country, i.e., Vietnam, given a dataset containing their statuses and current locations, detail down to provincial-or state-, city-level. The newly released algorithm reduced approximately 24.41% of processing time of the preceding one. In addition, the algorithm can be easily extended for supporting other countries given suitable datasets.
Vietnamese character recognition based on CNN model with reduced character classes Thi Ha Phan; Duc Chung Tran; Mohd Fadzil Hassan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2810

Abstract

This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O”. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness.
Development and testing of an FPT.AI-based voicebot Duc Chung Tran; Duc Long Nguyen; Mohd. Fadzil Hassan
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2620

Abstract

In recent years, voicebot has become a popular communication tool between humans and machines. In this paper, we will introduce our voicebot integrating text-to-speech (TTS) and speech-to-text (STT) modules provided by FPT.AI. This voicebot can be considered as a critical improvement of a typical chatbot because it can respond to human’s queries by both text and speech. FPT Open Speech, LibriSpeech datasets, and music files were used to test the accuracy and performance of the STT module. For the TTS module, it was tested by using text on news pages in both Vietnamese and English. To test the voicebot, Homestay Service topic questions and off-topic messages were input to the system. The TTS module achieved 100% accuracy in the Vietnamese text test and 72.66% accuracy in the English text test. In the STT module test, the accuracy for FPT open speech dataset (Vietnamese) is 90.51% and for LibriSpeech Dataset (English) is 0% while the accuracy in music files test is 0% for both. The voicebot achieved 100% accuracy in its test. Since the FPT.AI STT and TTS modules were developed to support only Vietnamese for dominating the Vietnam market, it is reasonable that the test with LibriSpeech Dataset resulted in 0% accuracy.
Co-Authors Duc Long Nguyen Mohd Fadzil Hassan Mohd. Fadzil Hassan Thi Ha Phan