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

Yeou Jiunn Chen, Jiunn Liang Wu, Hui Mei Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages4009-4013
Number of pages5
DOIs
Publication statusPublished - 2008 Nov 24
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 8

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period08-06-0108-06-08

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Chen, Y. J., Wu, J. L., & Yang, H. M. (2008). Automatic speech recognition and dependency network to identification of articulation error patterns. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 4009-4013). [4634374] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4634374