Open AccessHMM speech recognition study of an Improved Particle Swarm Optimization Based on Self-Adaptive Escape (AEPSO)
Author(s) -
Ye Liangpan,
Tao He
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/634/1/012074
Subject(s) - particle swarm optimization , computer science , schedule , hidden markov model , speech recognition , consistency (knowledge bases) , process (computing) , terminology , artificial intelligence , pattern recognition (psychology) , machine learning , operating system , linguistics , philosophy
Aiming at the single evaluation content of the current train simulation training system, the process of voice interaction in the daily work of train conductors cannot be effectively and comprehensively reflected, and the coordination between dispatching terminology and train running equipment are not considered. In order to realize the objective evaluation of whether the terms are clear and the semantics accurate and the consistency check of “schedule terms-equipment operation”, the HMM speech recognition subsystem of an Improved Particle Swarm Optimization Based on Self-Adaptive Escape (AEPSO) for railway dispatching was constructed. Compared with the traditional Baum-Welch algorithm, the experiment result shows that AEPSO-BW algorithm has certain advantages in speech recognition compared with traditional Baum-Welch algorithm. It provides a more comprehensive evaluation index for the training and assessment of vehicle service personnel.