Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks
The paper investigates the use of Singular and Modular Neural Networks in classifying the Malay syllable sounds in a speaker-independent manner. The syllable sounds are initialized with plosives and followed by vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. Linear Predictive...
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| Format: | Article |
| Language: | en |
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Penerbit UTM Press
2001
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| Online Access: | http://eprints.utm.my/1491/1/JT35D6.pdf http://eprints.utm.my/1491/ http://www.penerbit.utm.my/onlinejournal/35/D/JT35D6.pdf |
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| _version_ | 1845470484125188096 |
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| author | Ting, Hua Nong Yunus, Jasmy Shaikh Salleh, Sheikh Hussain |
| author_facet | Ting, Hua Nong Yunus, Jasmy Shaikh Salleh, Sheikh Hussain |
| author_sort | Ting, Hua Nong |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The paper investigates the use of Singular and Modular Neural Networks in classifying the Malay syllable sounds in a speaker-independent manner. The syllable sounds are initialized with plosives and followed by vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. Linear Predictive Coding (LPC) is used to extract the speech features. The Neural Networks utilize standard three-layer Multi-Layer Perceptron (MLP) as the speech sound classifier. The MLPs are trained with stochastic Back-Propagation (BP). The weights of the networks are updated after presentation of each training token and the sequence of the epoch is randomized after every epoch. The speech training and test tokens are obtained from 25 (17 females and 8 males) and 4 (all females) Malay adult speakers respectively. The total training and test token number are 1600 and 320 respectively. The result shows that modular neural networks outperform singular neural network with a recognition rate of about 92%. |
| format | Article |
| id | my.utm.eprints-1491 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2001 |
| publisher | Penerbit UTM Press |
| record_format | eprints |
| spelling | my.utm.eprints-14912017-11-01T04:17:48Z http://eprints.utm.my/1491/ Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks Ting, Hua Nong Yunus, Jasmy Shaikh Salleh, Sheikh Hussain TK Electrical engineering. Electronics Nuclear engineering The paper investigates the use of Singular and Modular Neural Networks in classifying the Malay syllable sounds in a speaker-independent manner. The syllable sounds are initialized with plosives and followed by vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. Linear Predictive Coding (LPC) is used to extract the speech features. The Neural Networks utilize standard three-layer Multi-Layer Perceptron (MLP) as the speech sound classifier. The MLPs are trained with stochastic Back-Propagation (BP). The weights of the networks are updated after presentation of each training token and the sequence of the epoch is randomized after every epoch. The speech training and test tokens are obtained from 25 (17 females and 8 males) and 4 (all females) Malay adult speakers respectively. The total training and test token number are 1600 and 320 respectively. The result shows that modular neural networks outperform singular neural network with a recognition rate of about 92%. Penerbit UTM Press 2001-12 Article PeerReviewed application/pdf en http://eprints.utm.my/1491/1/JT35D6.pdf Ting, Hua Nong and Yunus, Jasmy and Shaikh Salleh, Sheikh Hussain (2001) Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks. Jurnal Teknologi D (35D). pp. 65-76. ISSN 0127-9696 http://www.penerbit.utm.my/onlinejournal/35/D/JT35D6.pdf |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ting, Hua Nong Yunus, Jasmy Shaikh Salleh, Sheikh Hussain Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title | Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title_full | Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title_fullStr | Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title_full_unstemmed | Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title_short | Speaker-Independent Malay Syllable Recognition Using Singular And Modular Neural Networks |
| title_sort | speaker-independent malay syllable recognition using singular and modular neural networks |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/1491/1/JT35D6.pdf http://eprints.utm.my/1491/ http://www.penerbit.utm.my/onlinejournal/35/D/JT35D6.pdf |
| url_provider | http://eprints.utm.my/ |