• Phonetics and Speech Sciences
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• v.8 no.4
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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.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.316-324
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• 2020

It has experienced how difficult People with foreign language learning, it is to pronounce a new language different from the native language. The goal of various foreigners who want to learn Korean is to speak Korean as well as their native language to communicate smoothly. However, each native language's vocal habits also appear in Korean pronunciation, which prevents accurate information transmission. In this paper, the pronunciation of Chinese learners was compared with that of Korean. For comparison, the fundamental frequency and its variation of the speech signal were examined and the spectrogram was analyzed. The Formant frequencies known as the resonant frequency of the vocal tract were calculated. Based on these characteristics parameters, the classifier of the Supporting Vector Machine was found to classify the pronunciation of Koreans and the pronunciation of Chinese learners. In particular, the linguistic proposition was scientifically proved by examining the Korean pronunciation of /ㄹ/ that the Chinese people were not good at pronouncing.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.111-119
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• 2018

This paper proposes a method to improve speech recognition performance by extracting various pronunciations of the pseudo-morpheme unit from an eojeol unit corpus and generating a new recognition unit considering pronunciation variations. In the proposed method, we first align the pronunciation of the eojeol units and the pseudo-morpheme units, and then expand the pronunciation dictionary by extracting the new pronunciations of the pseudo-morpheme units at the pronunciation of the eojeol units. Then, we propose a new recognition unit that relies on pronunciation by tagging the obtained phoneme symbols according to the pseudo-morpheme units. The proposed units and their extended pronunciations are incorporated into the lexicon and language model of the speech recognizer. Experiments for performance evaluation are performed using the Korean speech recognizer with a trigram language model obtained by a 100 million pseudo-morpheme corpus and an acoustic model trained by a multi-genre broadcast speech data of 445 hours. The proposed method is shown to reduce the word error rate relatively by 13.8% in the news-genre evaluation data and by 4.5% in the total evaluation data.

• Cross-Cultural Studies
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• v.27
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• pp.7-29
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• 2012

This study was aimed to analyze - focusing on the thesis of Younes - the pronunciation error occuring mostly for Arabian speakers to learn French pronunciation for Arabians and to suggest the effective study plan to improve such errors and provide the effective studying method. The first part is on how the Arabic and French pronunciation system are distinguished, especially by comparing and analyzing the system of graphemes and phonemes, with which we focused on the fact that Arabian is a language centralized on consonants, while French is a verb-centered language. In the second part, we mainly discussed the cause and the types of errors occurring when Arabic speakers study French pronunciation. As of the category of mistakes, we separated them into consonants and verbs. We assumed the possible method which can be used in learning, focusing on /b/, /v/, /p/, /b/ - in case of non-verbs and consonants - and /y/, /ø/, - in case of verbs - which don't exist in Arabic pronunciation system. One of the troubles the professors in Arabian culture have in teaching French to native learners is how to solve the problem on a phonetic basis regarding speaking and reading ability, which belong to verbal skill, among the critical factors of foreign language education, which are listening, speaking, reading, and writing skills. In fact, the problems occuring in learning foreign language are had by not only Arabian learners but also general groups of people who learn the foreign language, the pronunciation system of which is distinctly distinguished from their mother tongue. The important fact professors should recognize regarding study of pronunciation is that they should encourage the learners to reach the acceptable level in proper communication rather than push them to have the same ability as the native speakers, Even though it cannot be said that the methods suggested in this study have absolute influence in reducing errors when learning French pronunciation system, I hope it can be at least a small help.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.119-122
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• 2000

In this paper, we implement an elementary system to correct accent, pronunciation, and intonation in English spoken by non-native English speakers. In case of the accent evaluation, energy and pitch information are used to find stressed syllables, and then we extract the segment information of input patterns using a dynamic time warping method to discriminate and evaluate accent position. For the pronunciation evaluation. we utilize the segment information using the same algorithm as in accent evaluation and calculate the spectral distance measure for each phoneme between input and reference. For the intonation evaluation. we propose nine pattern of slope to estimate pitch contour, then we grade test sentences by accumulated error obtained by the distance measure and estimated slope. Our result shows that 98 percent of accent and 71 percent of pronunciation evaluation agree with perceptual measure. As the result of the intonation evaluation. system represent the similar order of grade for the four sentences having different intonation patterns compared with perceptual evaluation.

• Speech Sciences
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• v.12 no.3
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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.

• Proceedings of the KSPS conference
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• 2004.05a
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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.

• Speech Sciences
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• v.11 no.2
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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.

• MALSORI
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• no.47
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• pp.51-72
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• 2003

The purpose of this paper is to design Speech Database for Korean pronunciation education. For this purpose, I investigated types of speech errors of Korean-learners, made texts for recording, which involves all types of speech errors, and showed how to gather speech data and how to tag their informations. It's natural that speech data should include Korean-learners' speech and Korean people's speech, because Speech DB that I try to develop is for teaching Korean pronunciation to foreigners. So this DB should have informations about speakers and phonetic informations, which are about phonetic value of segments and intonation of sentences. The intonation of sentence varies with the type of sentence, the structure of prosodic units, the length of a prosodic unit and so on. For this reason, Speech DB must involve tags about these informations.

• Language Facts and Perspectives
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• v.44
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• pp.357-397
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• 2018

The purpose of this paper is to investigate the phonemic errors in Korean learner's spoken corpus. Through this, we tried to investigate the common errors and the errors in certain languages. The results of the analysis were as follows. First, Errors that distinguish three phonemes(plain sound, tense sound, aspiration sound) were high in all languages. In the middle phonemes, the most common errors in pronouncing 'ㅓ' in all languages. Second, the errors of each language are different. Comparing the ratios by position, Chinese characters had the most common errors with 50% in final phoneme, and the Japanese language showed equal errors in initial, middle, and end. In English, initial phoneme errors accounted for 58%. Vietnamese Learners showed intensive errors in the initial and final phoneme. Third, in addition to the phoneme errors, we also examined the allophone errors and foreign language pronunciation errors. The allophone errors are mainly concentrated in 'ㄹ', ​​and the pronunciation of the foreign language is mainly used in the source language or the native language of the learners. This paper analyzes the phoneme errors in the Learner's spoken language through the spoken corpus data with representative and annotation consistency. Through this study, we could compare the difference of phoneme errors of Main Korean learners.