Automatic speech recognition and dependency network to identification of articulation error patterns

Yeou-Jiunn Chen, Jiunn-Liang Wu, Hui-Mei Yang
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
Articulation errors will seriously reduce speech intelligibility and the ease of spoken communication. Typically, a language therapist uses his or her clinical experience to identify articulation error patterns, a time-consuming and expensive process. This paper presents a novel automatic approach to identifying articulation error patterns and providing pronunciation error information to assist the linguistic therapist. A photo naming task is used to capture examples of an individual's
more » ... ion patterns. The collected speech is automatically segmented and labeled by a speech recognizer. The recognizer's pronunciation confusion network is adapted to improve the accuracy of the speech recognizer. The modified dependency network and a multiattribute decision model are applied to identify articulation error patterns. Experimental results reveal the usefulness of the proposed method and system.
doi:10.1109/ijcnn.2008.4634374 dblp:conf/ijcnn/ChenWY08 fatcat:d5nssr3wffbk3ehf2whdygm7bu