人才選拔乃是透過各種不同測驗媒介，吸引有興趣者前來應試，藉以選拔出合於業務需要之人才，以達適才適所目的之一系列活動。因此，如何善用不同遴選方法以選用不同種類人才；如何將人員的特性(如能力、經驗等)與工作職位的要求，做最適切的搭配，亦即「選賢任能」，考試方式之選用為主要關鍵。鑑於我國現行公務人員考選方式計有筆試、口試、測驗、實地考試、審查著作發明等多種，其中筆試及口試二種更是最常用的方法，前者係在測量應考人之學識能力，其實施過程只須命擬試題，試畢評閱試卷計分，雖簡便，惟無法測出一個人的人格、修養、品性、應對能力及處世態度。而「口試」適能彌補筆試無法測量的缺點，故對部分職務除施以筆試外，另須輔以口試，方能測出應考人的真正能力，選出所欲人才。目前我國公務人員考試實施口試者，係參照民國七十六年訂頒之「公務人員考試口試規則」相關規定辦理口試有關事宜，實施以來，作業尚稱嚴謹，惟社會大眾對於口試之評定因素、分數所佔比重及其實施過程等方面，仍多所批評，為確保口試之客觀及公平，以發揮其功能，遂依以口試之原理，探討我國公務人員考試加採口試者，其口試實施規定予以通盤檢討，同時參酌外國採用口試選才制度之特點及長處，以收「他山之石，可以攻錯」之旨，期使我國口試制度益臻完善。

Developing a time- and cost-efficient method for second language (L2) oral proficiency measurement is one of the research topics that has attracted much attention in recent decades. The purpose of this study is to develop a computerized oral testing system for L2 Japanese using automatic speech recognition (ASR) technology. Two testing methods called elicited imitation (EI) and simulated speech (SS) are proposed to quantify L2 accuracy and fluency via ASR processing. This study also suggests systematic EI item creation leveraging corpus technology and discusses the effectiveness of the test items created through analyses of item difficulty. Further, refinement of the EI grading system is described through a series of statistical investigations. For SS, this study reports the five most influential L2 fluency features identified through machine learning and proposes a method to yield individual SS scores with these features based on previous studies. Lastly, several methods to combine the EI and SS scores are presented to estimate L2 oral proficiency of Japanese.

The objective and automatic grading of oral language tests has been the subject of significant research in recent years. Several obstacles lie in the way of achieving this goal. Recent work has suggested a testing technique called elicited imitation (EI) can be used to accurately approximate global oral proficiency. This testing methodology, however, does not incorporate some fundamental aspects of language such as fluency. Other work has suggested another testing technique, simulated speech (SS), as a supplement to EI that can provide automated fluency metrics. In this work, I investigate a combination of fluency features extracted for SS testing and EI test scores to more accurately predict oral language proficiency. I also investigate the role of EI as an oral language test, and the optimal method of extracting fluency features from SS sound files. Results demonstrate the ability of EI and SS to more effectively predict hand-scored SS test item scores. I finally discuss implications of this work for future automated oral testing scenarios.

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