Fadhilah, Rosdi (2016) Fuzzy petri nets as a classification method for automatic speech intelligibility detection of children with speech impairments / Fadhilah Rosdi. PhD thesis, University of Malaya.
Abstract
The inability to speak fluently degrades the quality of life of many individuals. Early intervention from childhood can reduce disfluency of speech among adults. Traditionally, disfluency of speech among children is diagnosed based on speech intelligibility assessment by speech and language pathologists, which can be expensive and time consuming. Hence, numerous attempts were made to automate the speech intelligibility detection. While current detectors use statistical methods to discriminate unintelligible speech by calculating the posterior probability scores for each articulatory feature class, the major drawback is that the results are most likely to be based on training and input data, leading to inconsistencies in discriminating speech sounds. As such, the performance of detectors is below that of humans. To overcome this limitation, a new classification method based on Fuzzy Petri Net (FPN) is proposed to improve the classification accuracy. FPN was proposed as it has greater knowledge representation ability to reason using uncertain or ambiguous information. In this research, the speech features of Malay impaired children’s speech are analysed for the identification of the significant speech features in the impaired speech which are related to the intelligibility deficits. This research also presents how the intelligibility classes can be detected by FPN. The results showed that FPN is more reliable in discriminating speech sounds than the baseline classifiers with improvements in the classification accuracy, precision and recall.
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