Automatic pronunciation assessment of English produced by Korean learners using articulatory features

Ryu, Hyuksu; Chung, Minhwa

doi:10.13064/ksss.2016.8.4.103

Cited by 2 publications

(7 citation statements)

References 16 publications

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“…This kind of modeling has a limitation that it shows low performance when the category has multiple attributes, such as place, as discussed in [13]. On the contrary, aGOP features suggested in [10] and this study are computed based on each attribute, not category. For instance, our model classifies the articulatory attribute of 'alveolar' into a binary value of presence or absence.…”

Section: Articulatory Featuresmentioning

confidence: 98%

“…The study of [10] suggested articulatory-based GOPs for pronunciation assessment. The aGOP features are used to compare articulatory characteristics between natives and learners regarding the articulatory attributes.…”

Section: Articulatory Featuresmentioning

confidence: 99%

“…Articulatory features used in this study consist of 24 attributes such as sonorant and continuant (manner), labial and round (place), and voice (laryngeal). The details of articulatory features and the corresponding phones are presented in [10].…”

Section: Articulatory Featuresmentioning

confidence: 99%

“…The study of [10] used aGOP features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. In addition to well-known features such as Rate of Speech (ROS) in previous studies, [10] showed that the performance was improved by including aGOP features and applying the statistical method such as best subset selection.…”

Section: Articulatory Featuresmentioning

confidence: 99%

“…There are several studies utilizing articulatory features for pronunciation assessment and mispronunciation detection. The study of Ryu and Chung [10] proposes articulatory Goodness-Of-Pronunciations (aGOPs) as novel features for pronunciation assessment in English spoken by Korean learners. Furthermore, Li et al [5] extended GOP into speech attributes to detect mispronunciation of onset consonants in learners' Chinese by decision trees.…”

Section: Introductionmentioning

confidence: 99%

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Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features

Ryu¹,

Chung

2017

7th ISCA Workshop on Speech and Language Technology in Education (SLaTE 2017)

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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.

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Section: Articulatory Featuresmentioning

confidence: 98%

Section: Articulatory Featuresmentioning

confidence: 99%

Section: Articulatory Featuresmentioning

confidence: 99%

Section: Articulatory Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features

Ryu¹,

Chung

2017

7th ISCA Workshop on Speech and Language Technology in Education (SLaTE 2017)

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[Retracted] Russian Speech Conversion Algorithm Based on a Parallel Corpus and Machine Translation

Zhang

2022

Wireless Communications and Mobile Computing

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The phonetic conversion technology is crucial in the resource construction of Russian phonetic information processing. This paper explains how to build a corpus and the key algorithms that are used, as well as how to design auxiliary translation software and implement the key algorithms. This paper focuses on the “parallel corpus” method of problem solving and the indispensable role of a parallel corpus in Russian learning. This paper examines the foundations, motivations, and methods for using parallel corpora in translation instruction. The main way of using a parallel corpus in the classroom environment is to present data, so that learners can be exposed to a large amount of easily screened bilingual data, and translation skills and specific language item translation can be taught in a concentrated and focused manner. Among them, the creation of a large-scale Russian-Chinese parallel corpus will play an important role not only in improving the translation quality of Russian-Chinese machine translation systems but also in Chinese and Russian teaching as well as other branches of linguistics and translation studies, all of which should be given sufficient attention. This paper proposes the use of automatic speech analysis technology to assist Russian pronunciation learning and designs a Russian word pronunciation learning assistant system with demonstration, scoring, and feedback functions, in response to the shortcomings of pronunciation teaching in Russian teaching in China. It can provide corpus support for gathering a large number of parallel corpora and, in the future, enabling online translation. This system is used for corpus automatic construction, and future corpus automatic construction systems could be built on top of it. The proper application of parallel corpus data will aid in the development of a high-quality autonomous learning and translation teaching environment.

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Automatic pronunciation assessment of English produced by Korean learners using articulatory features

Cited by 2 publications

References 16 publications

Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features

Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features

[Retracted] Russian Speech Conversion Algorithm Based on a Parallel Corpus and Machine Translation

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