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. © 2013 Elsevier B.V.