Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization

Kit Yan Chan, Ka Fai Cedric Yiu, Tharam S. Dillon, Sven Nordholm, Sai Ho Ling

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy.
Original languageEnglish
Article number6164262
Pages (from-to)869-879
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number4
Publication statusPublished - 1 Nov 2012


  • Beamformer
  • intelligent fuzzy systems
  • particle swarm optimization
  • speech control
  • speech recognition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization'. Together they form a unique fingerprint.

Cite this