• 말소리와 음성과학
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• 제15권2호
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• pp.13-20
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• 2023

This study explores the potential of automated speech recognition (ASR) in assessing English learners' pronunciation. We employed ASR technology, acknowledged for its impartiality and consistent results, to analyze speech audio files, including synthesized speech, both native-like English and Korean-accented English, and speech recordings from a native English speaker. Through this analysis, we establish baseline values for the word error rate (WER). These were then compared with those obtained for human raters in perception experiments that assessed the speech productions of 30 first-year college students before and after taking a pronunciation course. Our sub-group analyses revealed positive training effects for Whisper, an ASR tool, and human raters, and identified distinct human rater strategies in different assessment aspects, such as proficiency, intelligibility, accuracy, and comprehensibility, that were not observed in ASR. Despite such challenges as recognizing accented speech traits, our findings suggest that digital tools such as ASR can streamline the pronunciation assessment process. With ongoing advancements in ASR technology, its potential as not only an assessment aid but also a self-directed learning tool for pronunciation feedback merits further exploration.

• 말소리와 음성과학
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• 제8권4호
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2000년도 7월 학술대회지
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• pp.338-349
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• 2000

Pronunciation teaching is once again coming into the foreground of ELT. Japan is, however, lagging far behind many countries in the development of pronunciation curricula and in the actual speech performance of the Japanese learners of English. The reasons for this can be found in the prevalence of communicative methodologies unfavorable for pronunciation teaching, in the lack of trained professionals, and in the large numbers of students in Japanese foreign language classes. This paper offers a way to promote foreign language pronunciation teaching in Japan and other countries by means of employing computer and internet facilities. The paper outlines the major directions of using modem speech technologies in pronunciation classes, like EVF (electronic visual feedback) training at segmental and prosodic levels; automated error detection, testing, grading and fluency assessment. The author discusses the applicability of some specific software packages (CSLU, SUGIspeech, Multispeech, Wavesurfer, etc.) for the needs of pronunciation teaching. Finally, the author talks about the globalization of pronunciation education via internet resources, such as computer corpora and speech and pronunciation training related web pages.

• 음성과학
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• 제12권3호
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• pp.185-200
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• 2005

The objective of this study is to examine acoustic cues in spoken French for the assessment of pronunciation which is necessary to realization of the multimedia system. The corpus is composed of simple expressions which consist of the French phonological system include all phonemes. This experiment was made on 4 male and female French native speakers and on 20 Korean speakers, university students who had learned the French language more than two years. We analyzed the recorded data by using spectrograph and measured comparative features by the numerical values. First of all, we found the mean and the deviation of all phonemes, and then chose features which had high error frequency and great differences between French and Korean pronunciations. The selected data were simplified and compared among them. After we judged whether the problems of pronunciation in each Korean speaker were either the utterance mistake or the interference of mother tongue, in terms of articulatory and auditory aspects, we tried to find acoustic features as simplified as possible. From this experiment, we could extract acoustic cues for the construction of the French pronunciation training system.

• 대한음성학회지:말소리
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• 제49호
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• pp.95-105
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• 2004

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. For this purpose we develope a speech recognizer which automatically classifies pronunciation errors when Koreans speak a foreign language. In order to develope the methods for automatic assessment of pronunciation quality, we propose a language model based score as a machine score in the speech recognizer. Experimental results show that the language model based score had higher correlation with human scores than that obtained using the conventional log-likelihood based score.

• ETRI Journal
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• 제35권1호
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• pp.89-99
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• 2013

The problem of the sentence-based pronunciation evaluation task is defined in the context of subjective criteria. Three subjective criteria (that is, the minimum subjective word score, the mean subjective word score, and first impression) are proposed and modeled with the combination of word-based assessment. Then, the subjective criteria are approximated with objective sentence pronunciation scores obtained with the combination of word-based metrics. No a priori studies of common mistakes are required, and class-based language models are used to incorporate incorrect and correct pronunciations. Incorrect pronunciations are automatically incorporated by making use of a competitive lexicon and the phonetic rules of students' mother and target languages. This procedure is applicable to any second language learning context, and subjective-objective sentence score correlations greater than or equal to 0.5 can be achieved when the proposed sentence-based pronunciation criteria are approximated with combinations of word-based scores. Finally, the subjective-objective sentence score correlations reported here are very comparable with those published elsewhere resulting from methods that require a priori studies of pronunciation errors.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2004년도 춘계 학술대회 발표논문집
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• pp.239-242
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we propose a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we also propose machine scoring methods for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• 음성과학
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• 제11권2호
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• pp.217-226
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we develop a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we propose a machine scoring method for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• 말소리와 음성과학
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• 제15권4호
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• pp.1-10
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• 2023

본 연구에서는 AI Hub에 구축된 '교육용 한국인의 영어 음성 데이터'에 있는 발음 평가 데이터를 활용하여 철자 기반 발화 속도 및 조음 속도와 음절 기반 발화 속도 및 조음 속도 중 발음 정확성 및 운율 유창성, 합산 점수를 예측하는 모델에 어떤 요소가 더 유의미한 영향을 미치는지 분석하였다. 이를 위해 13세, 19세, 26세 연령별, 성별, 수준별로 이 코퍼스의 훈련 데이터에서 총 900개 발화를 추출하여 데이터에 포함된 다양한 요소를 활용해 평가 점수를 예측하는 선형효과분석을 실행하였다. 선형효과분석에서 최적의 세 개 모델을 통해 예측된 평가 점수를 검증 데이터에서 추출한 총 180개 발화의 평가 점수와 얼마나 상관관계가 있는지도 분석하였다. 분석 결과 발음의 정확성과 운율의 유창성, 합산 점수 예측 모델 모두 철자 기반 발화 속도와 조음 속도보다 음절 기반 발화 속도와 조음 속도가 평가 점수를 예측하는데 더 큰 영향을 주는 것으로 밝혀졌다. 모델에서 예측한 점수와 검정 데이터의 실제 점수와의 상관계수는 .65에서 .68 사이로 각 모델의 평가 점수 예측력이 나쁘지 않았다. 발화 속도와 조음 속도 간에 어떤 요소가 더 큰 영향을 미치는지는 본 연구를 통해 밝혀내지 못하였다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.83-88
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• 2003

This study carried out an experimental English pronunciation assessment to see the differences in the relationship between the different rater categories. The result shows that i) correlation between Korean and Native American raters is high(r=.98) enough to be considered reliable, ii) previous instructions about assessment rubric and the knowledge about English phonetics and phonology exert little influence on the rating scores, iii) correlation between the automatic ILT(Interactive Language Tutor) rating using speech recognition technology and Natives' rating is stronger than that between ILT and Koreans' rating.