A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions

Kit Yan Chan, Pei Chee Yong, Sven Nordholm, Ka Fai Cedric Yiu, Hak Keung Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to interface with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions. The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories.
Original languageEnglish
Pages (from-to)132-139
Number of pages8
JournalApplied Soft Computing Journal
Volume14
Issue numberPART A
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • ANFIS
  • Commercial speech recognizer
  • Fuzzy neural networks
  • Noise suppression filter
  • Sigmoid filter
  • Speech enhancement
  • Speech recognition

ASJC Scopus subject areas

  • Software

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