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Automatic speech recognition and dependency network to identification of articulation error patterns
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'sdoi:10.1109/ijcnn.2008.4634374 dblp:conf/ijcnn/ChenWY08 fatcat:d5nssr3wffbk3ehf2whdygm7bu