scholarly journals Applying generation process model constraint to fundamental frequency contours generated by hidden-Markov-model-based speech synthesis

2012 ◽  
Vol 33 (4) ◽  
pp. 221-228 ◽  
Author(s):  
Tetsuya Matsuda ◽  
Keikichi Hirose ◽  
Nobuaki Minematsu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Fundamental Frequency ◽  
Speech Synthesis ◽  
Process Model ◽  
Hidden Markov ◽  
Generation Process ◽  
Model Based ◽  
Model Constraint

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

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

scholarly journals Hidden Markov Model based Speech Synthesis: A Review

2015 ◽  
Vol 130 (3) ◽  
pp. 35-39 ◽  
Author(s):  
Sangramsing Kayte ◽  
Monica Mundada ◽  
Jayesh Gujrathi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Speech Synthesis ◽  
Hidden Markov ◽  
Model Based
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