In this thesis, a novel hybrid Speech Recognition (SR) system called RUST (Recognition Using Syntactical Tree) is developed. RUST combines Artificial Neural Networks (ANN) with a Statistical Knowledge Source (SKS) for a small topic focused database. The hypothesis of this research work was that the inclusion of syntactic knowledge represented in the form of probability of occurrence of phones in words and sentences improves the performance of an ANN-based SR system. The lexicon of the first version of RUST (RUST-I) was developed with 1357 words of which 549 were unique. These words were extracted from three topics (finance, physics and general reading material), and could be expanded or reduced (specialised). The results of experiments carried out on RUST showed that by including basic statistical phonemic/syntactic knowledge with an ANN phone recognisor, the phone recognition rate was increased to 87% and word recognition rate to 78%. The first implementation of RUST was not optimal. Therefore, a second version of RUST (RUST-II) was implemented with an incremental learning algorithm and it has been shown to improve the phone recognition rate to 94%. The introduction of incremental learning to ANN-based speech recognition can be considered as the most innovative feature of this research. In conclusion this work has proved the hypothesis that inclusion of a phonemic syntactic knowledge of probabilistic nature and topic related statistical data using an adaptive phone recognisor based on neural networks has the potential to improve the performance of a speech recognition system. / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:ADTP/243045 |
| Date | January 2006 |
| Creators | Darjazini, Hisham, University of Western Sydney, College of Health and Science, School of Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
Page generated in 0.1078 seconds