This study investigated the relationship between phonemic awareness ability and performance in English listening comprehension. For the purpose of this research, two tests―a specially designed phonemic awareness test and an English L/C test―were used and a total of 268 high school students (132 males and 136 females) participated in this experiment. The statistical result clearly demonstrated that the ability to identify English phonemes was highly correlated with the English listening ability. It was also shown that there was a significant correlation between the two factors in a group of students showing a high level of L/C ability, whereas no significant correlation for the students with a low level of L/C ability. In addition, no major difference in the correlation was found between the male and female students.

This study aimεd to ìnvestigatε the relationship between phonemic awareness and listening comprehension ability. For this paπIC비ar research purpose thζ English name test and TOEFL LlC tests were employεd and a total of 34 university students participaκd in this εxpεriment. The English name tεst consisting of relativεly unheard 26 English namζs was specially dεsigned and adminisκrεd for the participants to depend on their own phonemic awarenεss ability, not applying their cognitive prior knowlεdge. whεn they transcribε:d the n잉nεs using the phonεtic alphabεts. Thε statistical result showed that thε Pearson's product-moment correlation coεfficient was .550* and its coefficient of determination was .303. This result c1ζarly demonstrated that the phonemic awareness ability was highly correlatεd with the overall Iistening comprεhension ability and implied thε need for implεmεnting the so-called bottom-up method for developing students' phonζrnic awareness ability at primary or secondary school levels.

In the early stages of reading, word decoding skills are prerequisite to reading. Word decoding involves word discrimination, phonemic decoding, and understanding of meaning. In this process, phonemic awareness enables students to examine language independently to manipulate its component sounds, and to use letter-sound correspondences to read and spell words. The 7th national curriculum of English and the primary school English textbooks fail to provide systematic language structure focus to facilitate efficiently student acquisition of the English phonemic system. Therefore, many students suffer from lack of word decoding development as well as encoding development, particularly in the 5th and 6th grades. This study explores the English phonemic awareness of Korean primary school students. To achieve the purpose of this study, a test for phonemic awareness was developed and Korean students' phonemic awareness was examined. The test deals with six areas: phonemic segmentation, phonemic manipulation, syllable splitting, blending, oddity, and invented spelling. The results are as follows: Firstly, many students showed successful results in phonemic segmentation and oddity. However, they have difficulties with syllable splitting and invented spelling. While students have strengths in the awareness of consonants except for r-l, f-p, and b-v, they suffer from lack of awareness of vowels. Secondly, errors of invented spelling and blending showed that students have differing development stages of phonemic awareness, e.g., some students master the phonemic system perfectly whereas others perceive only the alphabetic sounds. Finally, this research suggests that a balanced approach to reading is necessary in the early reading in the Korean context.

스마트 디바이스를 이용한 다양한 게임들이 증가하고 있는데, 음소인식은 스마트 디바이스를 사용한 효율적인 입력 방법은 음성이 될 수 있다. 게임에서 음성인식은 매우 빠르게 인식되면서 구동 되어야하는데, 본 연구에서는 게임 분야에서 유용하게 활용할 수 있는 최적화된 음소 인식 방법을 개발하였다. 본 논문에서 제안하는 음소 인식 방법은 음성 파장을 FFT로 전환하고, 해당 값을 Z평면에 도시한 후, 영역 데이터를 추출한 후 데이터베이스에 저장한다. 그리고 해당 값을 가중치 있는 두 갈래 그래프 최대 흐름 정합을 사용하여 음소 인식을 한다. 제안된 방법은 게임 또는 로봇과 같은 분야에서 빠른 음소 인식을 하고자 할 때 매우 유용한 방법이다.

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.