- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Hierarchical Phoneme Recognition Using Node-wise Relevance-Optimized Features | ||
| Basrah Journal for Engineering Science | ||
| Article 1, Volume 10, Issue 1, 2010, Pages 46-53 PDF (0 K) | ||
| Authors | ||
| lntessar T. Hwaidy; Ali E. Hameed | ||
| Abstract | ||
| In this paper, a hierarchical phoneme recognition system is proposed. The hierarchical approach is applied here to recursively partition the recognition problem into smaller and smaller sub-problems those are independently handled at the distinct nodes of the hierarchy. The nodes are individually set to characterize different properties of the input phoneme, or more precisely to make separate decisions on its pertinence to the different reference subgroups of phonemes. The full characterization of the input phoneme is achieved by traversing some root-to-leaf path through the hierarchy. The relationships between the different features of phonemes and their pertinence to the different reference subgroups are to be objectively characterized and optimized here. This involves specifying the decisive subset of features lbr each pertinence decision and neglecting the remaining features those are irrelevant to (or probably have negative effect on) that decision, at each node of the hierarchy. The optimization applied through the feature election process here, is not aimed at reducing the amount of features to he used in the recognition process, for the purpose of decreasing the time-complexity of the system, but, is interested in enhancing the decision making accuracy of the system by avoiding the misleading features. | ||
|
Statistics Article View: 66 PDF Download: 59 |
||