Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Grammatical Swarm: The generation of programs by social programming
Natural Computing: an international journal
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
INTEGRATION OF A VOICE RECOGNITION SYSTEM IN A SOCIAL ROBOT
Cybernetics and Systems
IEEE Transactions on Signal Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A robust adaptive speech enhancement system for vehicular applications
IEEE Transactions on Consumer Electronics
Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition
IEEE Transactions on Neural Networks
Speech Enhancement Based on Nonlinear Models Using Particle Filters
IEEE Transactions on Neural Networks
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Industrial automation with speech control functions is generally installed with a speech recognition sensor which is used as an interface for users to articulate speech commands. However, recognition errors are likely to be produced when background noise surrounds the command spoken into the speech recognition microcontrollers. In this paper, a speech enhancement strategy is proposed to develop noise suppression filters in order to improve the accuracy of speech recognition microcontrollers. It uses a universal estimator, namely a neural network, to enhance the recognition accuracy of microcontrollers by integrating better signals processed by various noise suppression filters, where a global optimization algorithm, namely an intelligent particle swarm optimization, is used to optimize the inbuilt parameters of the neural network in order to maximize accuracy of speech recognition microcontrollers working within noisy environments. The proposed approach overcomes the limitations of the existing noise suppression filters intended to improve recognition accuracy. The performance of the proposed approach was evaluated by a speech recognition microcontroller, which is used in electronic products with speech control functions. Results show that the accuracy of the speech recognition microcontroller can be improved using the proposed approach, when working under low signal to noise ratio conditions in the industrial environments of automobile engines and factory machines.