Mispronunciations in Graduate Students’ Presentation Projects

Megariani, Yeskha Martika; Listyantari, Nadia Aprilia; Bram, Barli

doi:10.31002/metathesis.v4i1.1879

Cited by 3 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was caused by the orthography in Bahasa Indonesia which rarely has a word with a consonant cluster at the end of the word, like /nt/ in the word /want/. This finding was also in line with the previous study by (Megariani et al, 2020) which found the same type of error in the word /ask/ that should be pronounced as /ɑːsk/ in British English and /aesk/ in American English, instead, students pronounced it as /ɑːs/.…”

Section: Percentage Of Students' Pronunciation Errorssupporting

confidence: 88%

“…When the teachers can analyze the students' errors, it will get easier in creating the learning materials to help cope with the student's difficulties in learning. Furthermore, many studies have examined the errors in students' pronunciations (Sembiring & Ginting, 2016;Ramasari, 2017;Simarmata & Pardede, 2018;and Megariani et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…It showed that students tended to carry-over pronunciation regularities from their first language or Bahasa Indonesia. It usually involves phonological changes, like the devoicing of final stop consonants or the spellingto-sound mapping of the mother tongue (Megariani et al, 2020). Students pronounced the words…”

Section: Percentage Of Students' Pronunciation Errorsmentioning

confidence: 99%

See 2 more Smart Citations

Pronunciation Errors in Students’ Vlog Projects

Martanti¹

2022

acit.j.teach.educ.

View full text Add to dashboard Cite

This research explored the pronunciation errors produced by the students in their vlog projects and the linguistics factors that contributed to their errors. This study employed a descriptive qualitative method. The data were collected from the vlogs made by the students which were uploaded on Youtube channels. The participants were 30 students of the Air Transport Management study program in STTKD Yogyakarta. The data collection technique was done by observation and documentation. The data were analyzed by applying qualitative data analysis. The data were classified using the theory from Bonaventura, Herron, and Menzel (2000). The results showed that there were 67 pronunciation errors existed in the vlog projects. The mispronunciations could be categorized into three types of errors, namely: the problems of non-native sounds as many as 34%, the carry-over of pronunciation regularities from the mother tongue as many as 45%, and the over-generalization of target language (L2) regularities as many as 21%. It is expected that the findings can help students diagnose their errors and mistakes in English pronunciation and later can motivate them to improve their English proficiencies. Besides, it can help the teachers develop strategies to minimize students’ errors in pronunciation.

show abstract

Section: Percentage Of Students' Pronunciation Errorssupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Section: Percentage Of Students' Pronunciation Errorsmentioning

confidence: 99%

See 1 more Smart Citation

Pronunciation Errors in Students’ Vlog Projects

Martanti¹

2022

acit.j.teach.educ.

View full text Add to dashboard Cite

show abstract

“…They also assumed that there were no mispronunciation errors due to the level of college students who have a basic understanding of pronunciation if there are still pronunciation errors. It is also supported by Megariani et al (2020) that many students did mispronunciations commonly when they did presentation projects. This has a relationship with the research conducted by the researcher, namely, students still use dialects in their home regions in English pronunciation.…”

Section: Difficulty In Pronouncing Consonants and Vocal Sounds Correctlymentioning

confidence: 87%

The Impacts of First Language on Students’ English Pronunciation

Octaviani,

Jannah,

Sebrina

et al. 2024

IJIET

View full text Add to dashboard Cite

This study aims to investigate how and why the first language impacts English Pronunciation Errors. The research design was a case study in the form of qualitative research using contrastive analysis principles to analyze the data. The participants selected were five students from various regions in Indonesia, that was Sunda, Batak, Jakarta, and Java. Each participant in this research studied English as a foreign language in the fourth semester at the Department of English Education at Universitas Tidar. Recorded pronunciation tests and interviews were used to collect data. Online phonetic transcription was used to find errors and transcribe them into phonetic symbols in this study. The finding of this study was the problems of long vowels, consonant sounds, words, and emphasis at the beginning, end, and middle have their respective differences according to their region. Even though the influence of the mother tongue on English pronunciation is quite strong, several methods and techniques can be applied to reduce problems in pronouncing English words.

show abstract

“…Megariani et al proposed that the components of phrase word bits include nouns, verbs, adjectives, and complement components. e word bits of the phrase are called patterns [15]. Balakrishnan et al [16] used the Bank of English corpus to summarize and analyze the valence patterns of 274 nouns, 511 verbs, and 544 adjectives in English and pointed out that the element code similarity in English coding method and pattern grammar is high.…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition and Extraction of English Verb Types Based on Index Line Clustering

Zhao

Kusaka²,

Wang³

2022

Mobile Information Systems

View full text Add to dashboard Cite

Languages are not uniform and certain words are used differently by speakers of different languages more or less often, or with distinct meanings. In both linguistics and natural language processing (NLP) problems, the classification that groups together verbs and a collection of similar syntactic and semantic features are of great interest. In the modern era of science and technology, NLP technology is developing rapidly. However, the interpretation of index lines still needs to be realized manually. This method takes a long time, especially after entering the era of big data, the number of corpora has increased rapidly and it is normal to have a corpus with hundreds of millions of words. The quantity of text generated every day is increasing intensely and the word index based on search words is as high as tens of thousands of lines, so it is very difficult to analyze index lines manually. Automatic lexical knowledge acquisition is essential for a variety of NLP activities. Particularly knowledge about verbs is critical, which are the major source of relationship information in a sentence. Due to this issue, this study attempts to automatically identify and extract English verbs by index line clustering. Each index behavior can be regarded as microtext automatic clustering to realize the automatic identification and extraction of English verb forms. This study first focuses on the clustering index algorithm including the C-means clustering algorithm and fuzzy C-means clustering algorithm, then describes in detail the automatic recognition and extraction process of English verbs based on index line clustering, and creates a verification set and completes the index line clustering of English verbs. Finally, the effect of index line algorithm is analyzed from two aspects: automatic recognition of English verb types and recall rate. At the same time, the verbs are selected to analyze their types and judge the probability of each type. The experimental results show that the average recognition rate of English verbs in the manual classification is 91.01%, and the average accuracy of automatic recognition and extraction of English verb patterns based on index row clustering is 95.99%.

show abstract

Mispronunciations in Graduate Students’ Presentation Projects

Cited by 3 publications

References 11 publications

Pronunciation Errors in Students’ Vlog Projects

Pronunciation Errors in Students’ Vlog Projects

The Impacts of First Language on Students’ English Pronunciation

Automatic Recognition and Extraction of English Verb Types Based on Index Line Clustering

Contact Info

Product

Resources

About