Purpose - Articulation errors substantially reduce speech intelligibility and the ease of spoken communication. Moreover, the articulation learning process that speech-language pathologists must provide is time consuming and expensive. The purpose of this paper, to facilitate the articulation learning process, is to develop a computer-aided articulation learning system to help subjects with articulation disorders. Design/methodology/approach - Facial animations, including lip and tongue animations, are used to convey the manner and place of articulation to the subject. This process improves the effectiveness of articulation learning. An interactive learning system is implemented through pronunciation confusion networks (PCNs) and automatic speech recognition (ASR), which are applied to identify mispronunciations. Findings - Speech and facial animations are effective for assisting subjects in imitating sounds and developing articulatory ability. PCNs and ASR can be used to automatically identify mispronunciations. Research limitations/implications - Future research will evaluate the clinical performance of this approach to articulation learning. Practical implications - The experimental results of this study indicate that it is feasible for clinically implementing a computer-aided articulation learning system in learning articulation. Originality/value - This study developed a computer-aided articulation learning system to facilitate improving speech production ability in subjects with articulation disorders.
|Number of pages||13|
|Journal||Engineering Computations (Swansea, Wales)|
|Publication status||Published - 2016|
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computational Theory and Mathematics