• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.73-84
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• 2009

Despite the growing interest among researchers in the field of second language (L2) phonological acquisition and its apparent contribution to linguistic and acquisition theories, there have been concerns about the lack of pedagogical application of the research findings in L2 classrooms (Levis, 1999, Derwing & Munro, 2005). Based on the belief that meeting an existing pedagogic need is something that should receive primary attention in SLA, this study attempts to bridge the gap between L2 pronunciation research and pronunciation pedagogy. In so doing, this study provides a narrative literature review of papers on L2 pronunciation published from 1994 to 2008 in Korea. The articles for review were retrieved from five database search engines. In addition, six journals where relevant articles most frequently appeared were selected and electronic searches of these six journals were conducted. A total of 117 articles which met the selection criteria were collected, and were reviewed to answer the following three research questions: a) What are the current research trends in L2 pronunciation in Korea? b) Do the research trends reflect a shift of focus on L2 pronunciation teaching? and c) What is the range of research practices in L2 pronunciation? The review of the papers indicates that the number of studies on L2 pronunciation increased sharply from 1999 to 2003. Some changes in research topics were also noticed. Research on segmental features of English was dominant from 1994 to 1998, but became more balanced with research on suprasegmentals from 2004 to 2008. This review also discusses the range of research practices in L2 pronunciation and makes suggestions for future directions in L2 pronunciation research.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.139-149
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• 2015

This paper aims to analyze pronunciation variations of loanwords produced by Korean and improve the performance of pronunciation modeling of loanwords in Korean by using syllable-based segmentation and phonological knowledge. The loanword text corpus used for our experiment consists of 14.5k words extracted from the frequently used words in set-top box, music, and point-of-interest (POI) domains. At first, pronunciations of loanwords in Korean are obtained by manual transcriptions, which are used as target pronunciations. The target pronunciations are compared with the standard pronunciation using confusion matrices for analysis of pronunciation variation patterns of loanwords. Based on the confusion matrices, three salient pronunciation variations of loanwords are identified such as tensification of fricative [s] and derounding of rounded vowel [ɥi] and [$w{\varepsilon}$]. In addition, a syllable-based segmentation method considering phonological knowledge is proposed for loanword pronunciation modeling. Performance of the baseline and the proposed method is measured using phone error rate (PER)/word error rate (WER) and F-score at various context spans. Experimental results show that the proposed method outperforms the baseline. We also observe that performance degrades when training and test sets come from different domains, which implies that loanword pronunciations are influenced by data domains. It is noteworthy that pronunciation modeling for loanwords is enhanced by reflecting phonological knowledge. The loanword pronunciation modeling in Korean proposed in this paper can be used for automatic speech recognition of application interface such as navigation systems and set-top boxes and for computer-assisted pronunciation training for Korean learners of English.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.29-36
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• 2010

The purposes of this study are (1) to see if language transfer effect is found in Korean speakers' pronunciation of English stops and to correct them and (2) to investigate the effectiveness of mimicry training and Speech Analyzer training on subjects' pronunciation of English stops. For these purposes, 20 Korean speakers' VOT values of English stops were measured using Speech Analyzer and their post-training production was compared with their pre-training production. The result shows that Korean speakers have no difficulty in correcting pronunciation errors of English voiceless stops and voiced stops and such a result indicates that language transfer effect is not noticed as expected. In addition, the result of pronunciation training shows that the training using Speech Analyzer is more effective than mimicry training.

• Phonetics and Speech Sciences
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• v.5 no.3
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• pp.31-38
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• 2013

This study investigated the influence of English vowel pronunciation training to English word-initial stop pronunciation. For that purpose, VOT values of English stops produced by twenty Korean English learners(five Youngnam dialect male speakers, five Youngnam dialect female speakers, five Kangwon dialect male speakers, and five Kangwon dialect female speakers) were measured using the Speech Analyzer and their post-training production was compared with their pre-training production. The result shows that post-training VOT values of voiced stops became closer to those of native English speakers in all four groups. Hence, it can be inferred that vowel pronunciation training is effective for correcting pronunciation of voiced vowels by analyzing the change of the quality of following vowels(especially low vowels) and the degree of giving stress.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.241-248
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• 2007

In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation. Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005; Lee 1998). This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation. In order to substituting rule-based generation with corpus-based one, an aligned corpus between an input and its pronunciation counterpart has been devised. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus.

• MALSORI
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• no.57
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• pp.1-14
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• 2006

This study examines personal factors that affect Korean speakers' English pronunciation. Personal factors which are examined here are as follows: personality type, cognitive system, motivational orientation type, interest in English, how often they listen to tapes, and academic achievements. Data were collected through MBTI (Myers Briggs Type Indicator) Test, Group Embedded Figural Test, and a Questionnaire. The participants consisted of 65 college students. All the results were statistically analyzed: Korean students' personality type and cognitive system are not related with their pronunciation, but motivational orientation type, how often they listen to tapes, academic achievements, and interest in English study are correlated with their pronunciation.

• MALSORI
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• no.49
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• pp.123-134
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• 2004

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. In this paper we propose an HMM based speech recognizer which automatically classifies pronunciation errors when Koreans speak Japanese. We also propose two pronunciation networks for automatic detection of mispronunciation. In this paper, we evaluated performances of the networks by computing the correlation between the human ratings and the machine scores obtained from the speech recognizer.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.275-276
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• 1996

Koreans learning Mandarin Chinese are faced with serious pronunciation errors in vowels, consonants and tones, etc. Most of these pronunciation errors are found to be due the transference of the native Korean phonetic habits. Following are some of the most common pronunciation errors encountered by Koreans learning Chinese.

• Cross-Cultural Studies
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• v.30
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• pp.97-124
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• 2013

For a long period, vowel reduction has been accepted as one of the most common pronunciation rules in Russian phonology. However, since the rules have been modified in many ways after the influx of loanwords, [a, e, i, o, u, ${\star}$]-including [e, o]-can now be pronounced in unstressed position, obeying the rule of vowel reduction. Especially in Modern Russian, along with the destruction of the consonant pronunciation norm due to some relatively complex changes it underwent palatalization, consonant pronunciation has been simplified, and as a response to such a phenomenon, the specialization of vowel pronunciation rule is now occurring. In other words, in the interrelation between consonants and vowels, as the pronunciation rules for consonants are simplified and thus the contrast between consonants is weakened, the degree of dependence on pronunciation of segment in the vowel pronunciation rule has been elevated. Therefore, the analysis says that the degree of vowel reduction depends on a vowel's distance from a stressed syllable is not enough; the influence of surrounding phonemes-including consonants-or the formative characteristics of words themselves should also be considered. The introduction of Max-noncorner/UnderLex, a/an Licence constraint that is related to non-declension nouns, and that of IdentC[back] and ShareCV[back], which are faithfulness constraint and share constraint respectively that are related to the nature of consonants stresses that vowel pronunciation rules should not be simply viewed as rules for vowels; The rules should be analyzed with emphasis on their correlation with surrounding phonemes.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.35-43
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• 2001

Many speech recognition systems have used pronunciation lexicon with possible multiple phonetic transcriptions for each word. The pronunciation lexicon is of often manually created. This process requires a lot of time and efforts, and furthermore, it is very difficult to maintain consistency of lexicon. To handle these problems, we present a model based on morphophon-ological analysis for automatically generating Korean pronunciation variants. By analyzing phonological variations frequently found in spoken Korean, we have derived about 700 phonemic contexts that would trigger the multilevel application of the corresponding phonological process, which consists of phonemic and allophonic rules. In generating pronunciation variants, morphological analysis is preceded to handle variations of phonological words. According to the morphological category, a set of tables reflecting phonemic context is looked up to generate pronunciation variants. Our experiments show that the proposed model produces mostly correct pronunciation variants of phonological words. Then we estimated how useful the pronunciation lexicon and training phonetic transcription using this proposed systems.