There are many collected phonetics which came from The Annotation of Classics in Jiyun. In The Annotation of Classics, the nature of Yinqie (音切) is very complex. There are some Yinqie not labeling phonetics for the prefixes. In Yinqie not labeling phonetics for the prefixes, some noted phonetics for variant, some noted phonetics for synonym, some noted phonetics for approximate character in form, some noted phonetics for notes and commentaries, some noted phonetics for original character. Of course, there are some Fanqie (反切) which noted phonetics for synonym in Jiyun. In the paper, we have specially discussed the Fanqie (反切) which noted phonetics for synonym in Jiyun. We divided the collected phonetics into two categories. We gave some examples for every category.

The present study aimed at analyzing item format, test content, and test trend in phonology and phonetics in the Korean public secondary school English teacher employment exams from 2014 to 2018 in comparison with those from 2009 to 2013. Findings of the study revealed that filling-out and restricted-response essay items have been evenly distributed on the test even though there was no extendedresponse essay item over the last 5 years. The items in phonology and phonetics were the second most frequent on the test (37.8%), followed by syntax and school grammar (57.3%), and its ratio tended to steadily increase since 2014. The frequency of test contents indicated that ‘consonants and vowels,’ ‘syllables,’ and ‘phonemes and allophones’ comprised high frequency while ‘phonological process,’ and ‘phonological rules’ has significantly decreased on the test. The new test trends linked to morphology and teaching methodology were qualitatively analyzed. Finally, problems of the current test and future direction were discussed.

자동 음성 인식(Automatic Speech Recognition)기술은 세계적인 의사소통과 협력을 원활히 할 수 있는 가능성을 제시한다. 현재까지 대부분의 연구들은 주로 사용되는 단일 언어의 말하기에만 집중되어 있다. 따라서 다른 언어들과 함께 사용되는 특정 ASR 시스템을 도입하는 데에는 비싼 비용이 뒤따른다. 본 논문은 다국어 음성 인식에 대한 일반적 접근으로 각 나라 언어를 대표한 발음사전(어휘모델)을 만들기 위하여 음성 인식에 이용하는 어휘 모델을 만들기 위하여 음소 언어 인식(PLI, Phonetic Language Identity) 형식의 입력된 파일을 해석하는 국제 음소 엔진(IPE, International Phoneticizing Engine)를 제안한다. IPE는 독립적이며 규칙을 기본으로 한다. 어휘모델 생성 과정은 Java 언어로 구현된 프로그램에 의해 이루어지고, 이 과정들은 규칙 상충을 줄여주며, 언어학적 훈련을 받지 않은 사람의 규칙 생성도 가능하게 한다. IPE에 의해 생성된 어휘모델을 연속 음성 인식기에 적용한 결과 우리말 인식률이 92.55%, 영어에 대하여 89.93%를 얻었다.