This paper seeks to analyze the passive voice in English textbooks in terms of sufficiency, sequencing, variety, interest, authenticity, and appropriateness. A total of thirty textbooks was randomly chosen for the analysis, six exemplars being selected from each grade from intermediate middle school through advanced high school. The results show that the subtypes of passives presented were substantial enough to fully understand the nature of the passive, and that the various sub-types of passives were sequenced reasonably. However, limitations were revealed in providing enough variety of activities/exercises on the one hand and of visual aids on the other. The textbooks were also found to contain several non-authentic language patterns such as wrong choice of the verb, collocation problems, etc. More serious was not only heavy reliance on sentence-level mechanical practices, particularly conversion exercises, but also the lack of attention paid to function-oriented instructions on the passive. These findings are expected to contribute to the effective design of new textbooks under the revised curriculum.

The present study aims to investigate how well Google Voice Actions, an automatic speech recognition system, recognizes Korean young English learners’ pronunciation of English words. To achieve this aim, the current study arranged for 18 Korean elementary school students to pronounce 219 English words and recorded their pronunciation. Then, the intelligibility of their pronunciation was measured using Google Voice Actions. The current study analyzed the measured intelligibility of Google Voice Actions in terms of the phonemic difficulty and familiarity the learners have with the words. The phonemic difficulty of each word was labelled as Group 1 to 5, depending on the number of difficult phonemic elements it contains. The familiarity of each word was also measured through a questionnaire. The findings revealed that the accuracy in Google Voice Actions’ recognition was closely related to the students’the phonemic difficulties and familiarity of the words. Interestingly, the pronunciation of words in Group 5 gained the highest recognition scores and that of Group 1 the second highest scores. Those of Group 2, 3, and 4 recorded lower recognition scores than the two groups. These results suggest that the more phonemic information the pronunciation of a word provides, the more relevant clues would be available for Google Voice Actions. This increases the possibility of successful search from the speech database.

본 논문에서 필자는 쇤베르크(Schoenberg, 1874-1951)의 《6개의 피아노 소곡》 Op. 19 No. 6을 보다 수평적인 관점의 ‘선율선’(musical line)에 초점을 맞춰 작품에 내재된 구조적 집합들 사이의 ‘성부진행’을 분석한다. 분석을 위해 택한 방법론은 스트라우스(Joseph Straus)의 오프셋(offset) 개념을 통한 성부진행 이론과 필자의 동일 구성음 배열 체계에 따른 최대 근접 성부진행 시스템이다. 이를 통해 필자는 작품의 수직적, 수평적 차원뿐만 아니라 미시적, 거시적 관점에 이르기까지 작품의 다양한 계층에서 매우 유기적으로 연관된 ‘수평적 성부진행’의 구조를 살펴볼 수 있었다. 또한 필자가 제안한 시스템이 무조음악 작품의 구조 적 응집성을 밝히는데 매우 유용하게 사용될 수 있음을 확인할 수 있었다. 같은 작품에 대한 다른 해석을 보여주는 필자의 시도는 새로운 분석 방법론을 통한 접근이 무조음악 안에 숨겨 진 또 다른 다양한 의미 해석의 가능성을 넓힐 수 있음을 보여준다.

The purpose of this paper is to explain the horizontal structure of Prelude, Op. 74, No. 4 by Skryabin objectively and logically by utilizing transformational networks to analyze the voice leading of this music. David Lewin explained transformational relations in musical space through a visual graph called a network and introduced a frame of analysis which simplified complicated aspects of music. There were other methods that analyse horizontal voice leading in atonal music by means of Lewin's transformational theory. Of these methods, Henry Klumpenhouwer's ‘transformational networks’ and Shougn J. O'Donnell's ‘dual transformation’ have been adopted as a primary analytical method for this article. The network of Prelude, Op. 74, No. 4 by Skryabin is composed of eleven types of set classes, made up of transposition transformation. The network has verified the similarity between pitch classes by utilizing isography, which shows its pitch-class contents. In this article, I hope to show that atonal music composition has a delicate structure.