This paper proposes a method to provide an articulatory diagnosis of English produced by Korean learners using articulatory Goodness-Of-Pronunciation (aGOP) features, which are based on the distinctive feature theory in phonology. Previous studies on mispronunciation diagnosis have mainly dealt with pronunciation errors at phone-level. They inform learners of which phone is recognized as a diagnosis, when the corresponding segment is realized as a mispronunciation. However, to provide learners more effective corrective feedback, diagnosis had better be performed at articulatory-level, such as place and manner of articulation, rather than at phone-level. This study aims to provide automatic articulatory diagnosis using articulationbased confidence scores. At first, the speech of learners is forced-aligned and recognized to compute the GOP and aGOPs. When the forced-aligned segment is a consonant, articulatory diagnosis is conducted in three articulatory categories: voicing, place of articulation, and manner of articulation. Otherwise, diagnosis is performed in terms of rounding, height, and backness corresponding to articulatory characteristics of vowels. Experimental results show that F1 scores for voicing, place, and manner corresponding to consonants are 0.828, 0.754, and 0.781, respectively, whereas F1 score for rounding, height, and backness corresponding to vowels are 0.843, 0.782, and 0.824, respectively. These results indicate that the proposed method yields effective articulatory diagnosis.
This study investigates the effects of Japanese learners' Korean segmental production on pronunciation evaluation by Korean native raters. Read speech from 24 learners whose native language is Japanese are transcribed at the phonemic level, and confusion matrices are generated based on the phonemic transcriptions. The deviance from the canonical pronunciation found in the learners' speech is analyzed in terms of phoneme substitutions, vowel insertions, and consonant deletions. Each learner's pronunciation is rated impressionistically by 5 Korean native raters. The result shows that the deviance from the canonical pronunciation is strongly correlated with the pronunciation evaluation scores. Especially, the rates of phoneme substitutions and vowel insertions which are very strongly correlated with the pronunciation evaluation scores.
The increasing demand for learning Korean as a foreign language yields a strong need for a CAPT system that is able to provide automatic tutoring. However, there is limited research on Korean pronunciation produced by non-natives. As a preliminary research towards developing a CAPT system for Chinese learners of Korean, we survey key findings of previous studies. And then, based on corpus analysis, we provide improved descriptions of segmental variation patterns of Korean produced by Chinese learners. The most salient variation is substitutions of liquid sounds: 33.0% of flap sounds were realized as lateral, and 35.0% of lateral sounds were realized as 3 major variation patterns. By quantifying all the variation patterns with statistical data, we resolve disagreements between previous studies, indicate new findings, which are important resources for developing a CAPT system, and lay the groundwork for Korean language learning for various L1 backgrounds.
This paper proposes a method for automatic pronunciation assessment of Korean spoken by L2 learners by selecting the best feature set from a collection of the most well-known features in the literature. The L2 Korean Speech Corpus is used for assessment modeling, where the native languages of the L2 learners are English, Chinese, Japanese, Russian, and Mongolian. In our system, learners' speech is forced-aligned and recognized using a native Korean acoustic model. Based on these results, various features for pronunciation assessment are computed, and divided into four categories such as RATE, SEGMENT, SILENCE, and GOP. Pronunciation scores produced by combining categories of features by multiple linear regression are used as a baseline. In order to enhance the baseline performance, relevant features are selected by using Principal Component Regression (PCR) and Best Subset Selection (BSS), respectively. The results show that the BSS model outperforms the baseline and the PCR model, and that features corresponding to speech segment and rate are selected as the relevant ones for automatic pronunciation assessment. The observed tendency of salient features will be useful for further improvement of automatic pronunciation assessment model for Korean language learners.
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