NN speech recognition utilizing aligned DTW local distance scores
This paper presents the neural network (NN) speech recognition using processed LPC input features. But NN has a limitation that the network must have a fixed amount of input nodes. The input feature processing method will use frame matching based on Dynamic Time Warping (DTW) algorithm to fix the in...
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| Language: | en |
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Universiti Teknologi Malaysia
2005
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| Online Access: | http://eprints.utm.my/1699/1/rubita05_ICMT-192.pdf http://eprints.utm.my/1699/ https://books.google.com.my/books/about/ICMT_2005.html?id=qj0UDAEACAAJ&redir_esc=y |
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| author | Sudirman, Rubita Salleh, Sh-Hussain Ting, Chee Ming |
| author_facet | Sudirman, Rubita Salleh, Sh-Hussain Ting, Chee Ming |
| author_sort | Sudirman, Rubita |
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| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper presents the neural network (NN) speech recognition using processed LPC input features. But NN has a limitation that the network must have a fixed amount of input nodes. The input feature processing method will use frame matching based on Dynamic Time Warping (DTW) algorithm to fix the input size to a fix amount of input vectors. The LPC features are aligned between the input frames (test set) to the reference (training set) using our DTW fixing frame (DTW-FF) algorithm. This proper time normalization is needed since NN is designed to compare data of the same length, whilst same speech can varies in their length. By doing frame fixing or also known as time normalization, the test set and the training set frames are adjusted so that both sets will have the same number of frames according to the reference set. The neural network with backpropagation algorithm is used as the recognition engine at the back-end processing to enhance the recognition performance. The results compare DTW with LPC coefficients to back-propagation NN with LPC coefficients adjusted using DTW. |
| format | Conference or Workshop Item |
| id | my.utm.eprints-1699 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2005 |
| publisher | Universiti Teknologi Malaysia |
| record_format | eprints |
| spelling | my.utm.eprints-16992017-08-28T00:14:48Z http://eprints.utm.my/1699/ NN speech recognition utilizing aligned DTW local distance scores Sudirman, Rubita Salleh, Sh-Hussain Ting, Chee Ming TK Electrical engineering. Electronics Nuclear engineering This paper presents the neural network (NN) speech recognition using processed LPC input features. But NN has a limitation that the network must have a fixed amount of input nodes. The input feature processing method will use frame matching based on Dynamic Time Warping (DTW) algorithm to fix the input size to a fix amount of input vectors. The LPC features are aligned between the input frames (test set) to the reference (training set) using our DTW fixing frame (DTW-FF) algorithm. This proper time normalization is needed since NN is designed to compare data of the same length, whilst same speech can varies in their length. By doing frame fixing or also known as time normalization, the test set and the training set frames are adjusted so that both sets will have the same number of frames according to the reference set. The neural network with backpropagation algorithm is used as the recognition engine at the back-end processing to enhance the recognition performance. The results compare DTW with LPC coefficients to back-propagation NN with LPC coefficients adjusted using DTW. Universiti Teknologi Malaysia 2005-12-04 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/1699/1/rubita05_ICMT-192.pdf Sudirman, Rubita and Salleh, Sh-Hussain and Ting, Chee Ming (2005) NN speech recognition utilizing aligned DTW local distance scores. In: Proceeding of the 9th International Conference on Mechatronics Technology, 5-8 December 2005, Kuala Lumpur. https://books.google.com.my/books/about/ICMT_2005.html?id=qj0UDAEACAAJ&redir_esc=y |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Sudirman, Rubita Salleh, Sh-Hussain Ting, Chee Ming NN speech recognition utilizing aligned DTW local distance scores |
| title | NN speech recognition utilizing aligned DTW local distance scores |
| title_full | NN speech recognition utilizing aligned DTW local distance scores |
| title_fullStr | NN speech recognition utilizing aligned DTW local distance scores |
| title_full_unstemmed | NN speech recognition utilizing aligned DTW local distance scores |
| title_short | NN speech recognition utilizing aligned DTW local distance scores |
| title_sort | nn speech recognition utilizing aligned dtw local distance scores |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/1699/1/rubita05_ICMT-192.pdf http://eprints.utm.my/1699/ https://books.google.com.my/books/about/ICMT_2005.html?id=qj0UDAEACAAJ&redir_esc=y |
| url_provider | http://eprints.utm.my/ |