Probabilistic Pronunciation Variation Model Based on Bayesian Network for Conversational Speech Recognition

2008 ◽  
Author(s):  
Sakriani Sakti ◽  
Konstantin Markov ◽  
Satoshi Nakamura
Keyword(s):  
Speech Recognition ◽  
Bayesian Network ◽  
Conversational Speech ◽  
Pronunciation Variation ◽  
Model Based ◽  
Variation Model

Densely Connected Networks for Conversational Speech Recognition

2018 ◽  
Author(s):  
Kyu Han ◽  
Akshay Chandrashekaran ◽  
Jungsuk Kim ◽  
Ian Lane
Keyword(s):  
Speech Recognition ◽  
Conversational Speech

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

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