Identification of Articulation Error Patterns Using a Novel Dependence Network

Articulation errors seriously reduce speech intelligibility and the ease of spoken communication. Speech-language pathologists manually identify articulation error patterns based on their clinical experience, which is a time-consuming and expensive process. This study proposes an automatic pronunciation error identification system that uses a novel dependence network (DN) approach. In order to derive a subject's articulatory information, a photo naming task is performed to obtain the subject's speech patterns. Based on clinical knowledge about speech evaluation, a DN scheme was used to model the relationships of a test word, a subject, a speech pattern, and an articulation error pattern. To integrate DN into automatic speech recognition (ASR), a pronunciation confusion network is proposed to model the probability of DN and is then used to guide the search space of the ASR. Further, to increase the accuracy of the ASR, an appropriate threshold based on a histogram of pronunciation errors is selected in order to disregard rare pronunciation errors. Finally, the articulation error patterns were well identified by integrating the likelihoods of the DNs of each phoneme. The results of this study indicate that it is feasible to clinically implement this dynamic network approach to achieve satisfactory performance in articulation evaluation.