Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/26471 |
| _version_ | 1848751995965407232 |
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| author | Chan, Kit Yan Nordholm, Sven Yiu, Ka Fai Togneri, R. |
| author_facet | Chan, Kit Yan Nordholm, Sven Yiu, Ka Fai Togneri, R. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:01:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-26471 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:01:35Z |
| publishDate | 2013 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-264712019-02-19T05:35:39Z Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments Chan, Kit Yan Nordholm, Sven Yiu, Ka Fai Togneri, R. particle swarm optimization noise suppression filters acoustic signal enhancement speech control background noise neural networks Speech recognition microcontroller 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 Journal Article http://hdl.handle.net/20.500.11937/26471 10.1016/j.neucom.2013.03.008 Elsevier BV fulltext |
| spellingShingle | particle swarm optimization noise suppression filters acoustic signal enhancement speech control background noise neural networks Speech recognition microcontroller Chan, Kit Yan Nordholm, Sven Yiu, Ka Fai Togneri, R. Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title | Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title_full | Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title_fullStr | Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title_full_unstemmed | Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title_short | Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments |
| title_sort | speech enhancement strategy for speech recognition microcontroller under noisy environments |
| topic | particle swarm optimization noise suppression filters acoustic signal enhancement speech control background noise neural networks Speech recognition microcontroller |
| url | http://hdl.handle.net/20.500.11937/26471 |