• MALSORI
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• no.29_30
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• pp.1-18
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• 1995

미국 남부 방언은 그 지역의 광대함과 아울러 그 지역에 속하는 언어사용자들의 언어 문화 및 역사적인 다양성에 의해 결코 한 가지 방언으로 취급할 수 없는 것임에도 불구하고 많은 경우에 그렇게 다루어져 왔다. 특히 소위 '남부 방언의 특징적 요소'로서 몇몇 자질들에 대한 연구가 많이 이루어져 왔다. 본 논문의 목적은 텍사스 지역방언에 이러한 남부 방언의 특징적 자질이 어느 정도 유지되고 있는가를 알아보고, 아울러 두 세대간에 언어적 차이가 있는지, 있다면 그 변화의 방향은 어느 쪽으로 전개되어가고 있는지를 밝히려는 데 있다. 이를 위하여 토박이 텍사스 인에 한하여 한 가정에서 두 세대(늙은 세대와 젊은 세대)를 대표하는 정보제공자 두 명씩을 각각 추출하여 네 가정 모두 여덟 명에게서 얻은 언어자료를 녹음하여 이를 분석, 정리하였다. 텍사스 지역방언에 대해 밝혀진 주요 내용은 다음과 같다. 1. /l/앞에 나오는 단순모음 /i/는 [$r{\partial}$] 또는 [$r{\partial}$]로 이중모음화된다. 2. 강세음절에서 비음 앞에 나오는 /e/와 /I/는 중화된다. 3. 늙은 세대에서는 /a/와 /${\supset}$/가 융합되어 쓰이나, 젊은 세대에서는 융합이 일어나지 않는다. 4. 이중모음 /ar/는 /a:/또는 /a/로 단순모음화하는 것으로 보인다. 5. 이중로음 /$a{\mho}$/ /$o{\mho}$/의 앞모음이 전설화한다. 6. [u], [ju] 와 [${\mho}$]는 모두 [${\mho}$]로 된다. 7. [w] 와 [M]는 일관성 없이 교대로 사용되나 [M]는 특히 늙은 세대에서 더 많이 사용된다.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.520-529
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• 2018

The purpose of this study is to investigate the effect of Jeju dialect of service worker on tourist image, tourist satisfaction and revisit intention. The Regional dialect can be viewed as a cultural element that characterizes the region, It also serves as a medium to inform tourists of the feelings they experience while they are out of their area and visiting other areas. As a result of the study, it was found that the communication factors in the language communication of dialects had a positive(+) effect on the cognitive and emotional images of tourist sites. Interesting factors showed positive(+) effect on cognitive image of sightseeing spot, but did not affect emotional image. As a result of the study, it should be noted that excessive use of regional dialects may not necessarily have a positive effect on the emotions of tourists. If you want to develop tourist products using dialects, you need to pay attention to the use of words and expressions so that there is no misunderstanding.

• Lingua Humanitatis
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• v.8
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• pp.291-313
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• 2006

Previously, studies concerning the Russian dialects have been mainly focused on northern, central, and southern dialects limited to western Russia of Ural Mountains. On the contrary, the Siberian and Far-eastern dialects have been completely disregarded to the main stream of the Russian dialectology. As a result of a poll concerning this idea, the majority has answered that there is no dialect in Siberian and Far-east regions. Though the reasons for the outcome of the poll could vary, it could not be simply accepted that there is no dialect in such vast regions. Thus, a survey has took place to examine the existence of dialects in the regions of Siberia and Far-east. The first phase of the survey inquired the residents of the regions including Siberia and Far-east to respond to questions regarding 83 vocabularies on wedding in contrast to the regions covering western Ural and Moscow. The 23 informants were residents of the concerned regions who have come to visit Pushkin National Institute of Russian Language and, others, Korea. The questionnaires used in this survey were partly obtained from the questionnaires originated by the Language Institute of St. Petersburg National University. Although the limited range of regions and a small number of respondents who partook in this survey could raise some issues on the table, it is relevant to understand that this study would open up the path for the development of studies concerning regional dialects in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.37-43
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• 2021

Using the acoustic features of speech, important social and linguistic information about the speaker can be obtained, and one of the key features is the dialect. A speaker's use of a dialect is a major barrier to interaction with a computer. Dialects can be distinguished at various levels such as phonemes, syllables, words, phrases, and sentences, but it is difficult to distinguish dialects by identifying them one by one. Therefore, in this paper, we propose a lightweight Korean dialect classification model using only MFCC among the features of speech data. We study the optimal method to utilize MFCC features through Korean conversational voice data, and compare the classification performance of five Korean dialects in Gyeonggi/Seoul, Gangwon, Chungcheong, Jeolla, and Gyeongsang in eight machine learning and deep learning classification models. The performance of most classification models was improved by normalizing the MFCC, and the accuracy was improved by 1.07% and F1-score by 2.04% compared to the best performance of the classification model before normalizing the MFCC.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.819-829
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• 2017

In this paper, we describe the current state of speech recognition technology and identify the basic speech recognition technology and algorithms first, and then explain the code flow of API necessary for speech recognition technology. We use the application programming interface (API) of Google, Naver, and Daum KaKao, which have the most famous search engine among the speech recognition APIs, to create a voice recognition app in the Android studio tool. Then, we perform a speech recognition experiment on people's standard words and dialects according to gender, age, and region, and then organize the recognition rates into a table. Experiments were conducted on the Gyeongsang-do, Chungcheong-do, and Jeolla-do provinces where the degree of tongues was severe. And Comparative experiments were also conducted on standardized dialects. Based on the resultant sentences, the accuracy of the sentence is checked based on spacing of words, final consonant, postposition, and words and the number of each error is represented by a number. As a result, we aim to introduce the advantages of each API according to the speech recognition rate, and to establish a basic framework for the most efficient use.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.566-573
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• 1996

• MALSORI
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• no.31_32
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• pp.97-122
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• 1996

• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.332-339
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• 2007

이 논문은 21세기 세종계획 중 1998년부터 2007년까지 한민족언어정보화 분과 사업의 성과와 그 활용을 제시한 것이다. 이 사업에서는 국어 어문규정 검색 프로그램, 남북한 언어 비교사전, 한국 방언 검색 프로그램, 국어의 어휘 역사 검색 프로그램, 문학작품에 나타난 방언 검색 프로그램, 한국 전통문화 어휘 검색 프로그램, 남북한 정서법 변환 프로그램 등을 만들어 활용하였다. 국어 어휘와 관련된 정보화 작업을 시행하여, 국어를 다양하게 정보화하는 인력을 양성하고, 국어 어휘의 종합적 연구와 국어 발전에 크게 기여하였고, 국민들이 어문규정을 손쉽게 검색하여 국어생활에 큰 도움이 되도록 하였다. 특히 남북한 어휘 연구를 통하여 언어 통일 문제를 다루었고, 국어의 역사적 연구, 각 지역 방언 및 문학작품에 나타난 방언의 연구 및 이해에 큰 도움이 되도록 하였다.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.169-177
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• 2005

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.43-51
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• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.