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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.

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.

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.

Future research will evaluate the clinical performance of this approach to articulation learning.

The experimental results of this study indicate that it is feasible for clinically implementing a computer-aided articulation learning system in learning articulation.

This study developed a computer-aided articulation learning system to facilitate improving speech production ability in subjects with articulation disorders.

The paper's aim is to propose a core journal decision method, called the local impact factor (LIF), which can evaluate the requirements of the local user community by combining both the access rate and the weighted impact factor, and by tracking citation information on the local users' articles.

Many institutions with a limited budget can subscribe only to the most valuable journals for their users. The importance of a journal to a local community can be calculated in many ways. This paper takes both global and local access frequency and journal citations into consideration. The method of weighted web page link analysis is adopted.

This paper finds that the weighted page rank may be used efficiently in the core journal decisions. Experimental results demonstrate that the proposed LIF can effectively suggest journals to local users better than existing methods (i.e. impact factor or the local journal rank).

This research requires the determination of the thesis scores, which needs authorisation from the authors. If the scores are not available, the scores may be subjectively assigned or retrieved from the other resources.

A case study in National Cheng Kung University was conducted to show that the LIF can be used to help library managers evaluate the real demands of local community users.

Rather than existing research, this paper focuses on the utilisation and requirements of local community users and also finds the contributions of citation information to be significant and critical.