Automatic speech recognition and dependency network to identification of articulation error patterns
Yeou‐Jiunn Chen, Jiunn‐Liang Wu, Hui-Mei Yang · 2008
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 error information of pronunciation to assist the linguistic therapist. A photo naming task is used to capture examples of an individual’s articulation 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.