Optimal state duration assignment in hidden Markov model‐based text‐to‐speech synthesis system

2015 ◽  
Vol 51 (12) ◽  
pp. 941-943 ◽  
Author(s):  
Najeeb Ullah Khan ◽  
Jung‐Chul Lee
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Speech Synthesis ◽  
Hidden Markov ◽  
Text To Speech ◽  
Synthesis System ◽  
Optimal State ◽  
Model Based ◽  
State Duration ◽  
Text To Speech Synthesis

Toward hidden Markov model‐based spontaneous speech synthesis

2006 ◽  
Vol 120 (5) ◽  
pp. 3037-3038
Author(s):  
Tatsuya Akagawa ◽  
Koji Iwano ◽  
Sadaoki Furui
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Speech Synthesis ◽  
Hidden Markov ◽  
Spontaneous Speech ◽  
Model Based

scholarly journals Development of Vietnamese Speech Synthesis System using Deep Neural Networks

2019 ◽  
Vol 34 (4) ◽  
pp. 349-363 ◽  
Author(s):  
Thinh Van Nguyen ◽  
Bao Quoc Nguyen ◽  
Kinh Huy Phan ◽  
Hai Van Do
Keyword(s):  
Neural Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Speech Synthesis ◽  
Deep Neural Networks ◽  
Hidden Markov ◽  
Experimental Results ◽  
Training Data ◽  
The Internet ◽  
Synthesis System

In this paper, we present our first Vietnamese speech synthesis system based on deep neural networks. To improve the training data collected from the Internet, a cleaning method is proposed. The experimental results indicate that by using deeper architectures we can achieve better performance for the TTS than using shallow architectures such as hidden Markov model. We also present the effect of using different amounts of data to train the TTS systems. In the VLSP TTS challenge 2018, our proposed DNN-based speech synthesis system won the first place in all three subjects including naturalness, intelligibility, and MOS.

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