The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.]
This paper describes speech recognizer modeling techniques which are suited to high performance and robust isolated word recognition in speaker-independent manner. In this study, a speech recognition system is presented, specifically for an isolated spoken Malay word recognizer which uses spontaneou...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Computer and Mathematical Sciences
2010
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/11106/ |
| _version_ | 1848802973493231616 |
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| author | Seman, Noraini Abu Bakar, Zainab Abu Bakar, Nordin Mohamed, Haslizatul Fairuz Abdullah, Nur Atiqah Sia Prasanna, Ramakrisnan Syed Ahmad, Sharifah Mumtazah |
| author_facet | Seman, Noraini Abu Bakar, Zainab Abu Bakar, Nordin Mohamed, Haslizatul Fairuz Abdullah, Nur Atiqah Sia Prasanna, Ramakrisnan Syed Ahmad, Sharifah Mumtazah |
| author_sort | Seman, Noraini |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | This paper describes speech recognizer modeling techniques which are suited to high performance and robust isolated word recognition in speaker-independent manner. In this study, a speech recognition system is presented, specifically for an isolated spoken Malay word recognizer which uses spontaneous and formal speeches collected from Parliament of Malaysia. Currently the vocabulary is limited to ten words that can be pronounced exactly as it written and control the distribution of the vocalic segments. The speech segmentation task is achieved by adopted energy based parameter and zero crossing rate measure with modification to better locates the beginning and ending points of speech from the spoken words. The training and recognition processes are realized by using Multi-layer Perceptron (MLP) Neural Networks with two-layer feedforward network configurations that are trained with stochastic error back-propagation to adjust its weights and biases after presentation of every training data. The Mel-frequency Cepstral Coefficients (MFCCs) has been chosen as speech extraction approach from each segmented utterance as characteristic features for the word recognizer. The MLP performance to determine the optimal cepstral orders and hidden neurons numbers are analyzed. Recognition results showed that the performance of the two-layer network increased as the numbers of hidden neurons increased. Experimental result also showed that the cepstral orders of 12 to 14 were appropriate for the speech feature extraction for the data in this study. |
| first_indexed | 2025-11-14T21:31:51Z |
| format | Article |
| id | uitm-11106 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:31:51Z |
| publishDate | 2010 |
| publisher | Faculty of Computer and Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-111062022-06-14T02:43:06Z https://ir.uitm.edu.my/id/eprint/11106/ The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] mjoc Seman, Noraini Abu Bakar, Zainab Abu Bakar, Nordin Mohamed, Haslizatul Fairuz Abdullah, Nur Atiqah Sia Prasanna, Ramakrisnan Syed Ahmad, Sharifah Mumtazah Neural networks (Computer science) This paper describes speech recognizer modeling techniques which are suited to high performance and robust isolated word recognition in speaker-independent manner. In this study, a speech recognition system is presented, specifically for an isolated spoken Malay word recognizer which uses spontaneous and formal speeches collected from Parliament of Malaysia. Currently the vocabulary is limited to ten words that can be pronounced exactly as it written and control the distribution of the vocalic segments. The speech segmentation task is achieved by adopted energy based parameter and zero crossing rate measure with modification to better locates the beginning and ending points of speech from the spoken words. The training and recognition processes are realized by using Multi-layer Perceptron (MLP) Neural Networks with two-layer feedforward network configurations that are trained with stochastic error back-propagation to adjust its weights and biases after presentation of every training data. The Mel-frequency Cepstral Coefficients (MFCCs) has been chosen as speech extraction approach from each segmented utterance as characteristic features for the word recognizer. The MLP performance to determine the optimal cepstral orders and hidden neurons numbers are analyzed. Recognition results showed that the performance of the two-layer network increased as the numbers of hidden neurons increased. Experimental result also showed that the cepstral orders of 12 to 14 were appropriate for the speech feature extraction for the data in this study. Faculty of Computer and Mathematical Sciences 2010 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/11106/1/11106.pdf Seman, Noraini and Abu Bakar, Zainab and Abu Bakar, Nordin and Mohamed, Haslizatul Fairuz and Abdullah, Nur Atiqah Sia and Prasanna, Ramakrisnan and Syed Ahmad, Sharifah Mumtazah (2010) The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.]. (2010) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29.html>, 1 (1). pp. 1-9. ISSN 2231-7473 https://mjoc.uitm.edu.my/ |
| spellingShingle | Neural networks (Computer science) Seman, Noraini Abu Bakar, Zainab Abu Bakar, Nordin Mohamed, Haslizatul Fairuz Abdullah, Nur Atiqah Sia Prasanna, Ramakrisnan Syed Ahmad, Sharifah Mumtazah The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title | The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title_full | The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title_fullStr | The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title_full_unstemmed | The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title_short | The optimal performance of multi-layer neural network for speaker-independent isolated spoken Malay parliamentary speech / Noraini Seman Zainab Abu Bakar ...[et al.] |
| title_sort | optimal performance of multi-layer neural network for speaker-independent isolated spoken malay parliamentary speech / noraini seman zainab abu bakar ...[et al.] |
| topic | Neural networks (Computer science) |
| url | https://ir.uitm.edu.my/id/eprint/11106/ https://ir.uitm.edu.my/id/eprint/11106/ |