• MALSORI
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• no.65
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• pp.93-103
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• 2008

In this paper, we propose a method for optimizing a multiple pronunciation dictionary used for modeling pronunciation variations of non-native speech. The proposed method removes some confusable pronunciation variants in the dictionary, resulting in a reduced dictionary size and less decoding time for automatic speech recognition (ASR). To this end, a confusability measure is first defined based on the Levenshtein distance between two different pronunciation variants. Then, the number of phonemes for each pronunciation variant is incorporated into the confusability measure to compensate for ASR errors due to words of a shorter length. We investigate the effect of the proposed method on ASR performance, where Korean is selected as the target language and Korean utterances spoken by Chinese native speakers are considered as non-native speech. It is shown from the experiments that an ASR system using the multiple pronunciation dictionary optimized by the proposed method can provide a relative average word error rate reduction of 6.25%, with 11.67% less ASR decoding time, as compared with that using a multiple pronunciation dictionary without the optimization.

• Phonetics and Speech Sciences
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• v.2 no.2
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• pp.17-25
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• 2010

This study explores whether Koreans exploit their native coda-neutralization process when recognizing words in Korean continuous speech. According to the phonological rules in Korean, coda-neutralization process must come before the liaison process, as long as the latter(i.e. liaison process) occurs between 'words', which results in liaison-consonants being coda-neutralized ones such as /b/, /d/, or /g/, rather than non-neutralized ones like /p/, /t/, /k/, /ʧ/, /ʤ/, or /s/. Consequently, if Korean listeners use their native coda-neutralization rules when processing speech input, word recognition will be hampered when non-neutralized consonants precede vowel-initial targets. Word-spotting and word-monitoring tasks were conducted in Experiment 1 and 2, respectively. In both experiments, listeners recognized words faster and more accurately when vowel-initial target words were preceded by coda-neutralized consonants than when preceded by coda non-neutralized ones. The results show that Korean listeners exploit the coda-neutralization process when processing their native spoken language.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.115-121
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• 2011

This paper examines how L2 speech production varies according to learner's L2 proficiency level. L2 speech production variations are analyzed by quantitative measures at word and phone levels using Korean learners' English corpus. Word-level variations are analyzed using correctness to explain how speech realizations are different from the canonical forms, while accuracy is used for analysis at phone level to reflect phone insertions and deletions together with substitutions. The results show that speech production of learners with different L2 proficiency levels are considerably different in terms of performance and individual realizations at word and phone levels. These results confirm that speech production of non-native speakers varies according to their L2 proficiency levels, even though they share the same L1 background. Furthermore, they will contribute to improve non-native speech recognition performance of ASR-based English language educational system for Korean learners of English.

• ETRI Journal
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• v.42 no.5
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• pp.761-772
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• 2020

This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced-alignment step using a native AM and non-native AM, and (c) a linear regression-based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un-segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

• ETRI Journal
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• v.46 no.1
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• pp.48-58
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• 2024

This paper presents the development of language tutoring systems for nonnative speakers by leveraging advanced end-to-end automatic speech recognition (ASR) and proficiency evaluation. Given the frequent errors in non-native speech, high-performance spontaneous speech recognition must be applied. Our systems accurately evaluate pronunciation and speaking fluency and provide feedback on errors by relying on precise transcriptions. End-to-end ASR is implemented and enhanced by using diverse non-native speaker speech data for model training. For performance enhancement, we combine semisupervised and transfer learning techniques using labeled and unlabeled speech data. Automatic proficiency evaluation is performed by a model trained to maximize the statistical correlation between the fluency score manually determined by a human expert and a calculated fluency score. We developed an English tutoring system for Korean elementary students called EBS AI Peng-Talk and a Korean tutoring system for foreigners called KSI Korean AI Tutor. Both systems were deployed by South Korean government agencies.

• Journal of the Korea Convergence Society
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• v.5 no.1
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• pp.35-41
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• 2014

An automatic speech recognition system is one of the popular research problems. There are many research groups working in this field for different language including Japanese. Japanese vowel recognition is one of important parts in the Japanese speech recognition system. The vowel classification system with the Mamdani fuzzy inference system was developed in this research. We tested our system on the blind test data set collected from one male native Japanese speaker and four male non-native Japanese speakers. All subjects in the blind test data set were not the same subjects in the training data set. We found out that the classification rate from the training data set is 95.0 %. In the speaker-independent experiments, the classification rate from the native speaker is around 70.0 %, whereas that from the non-native speakers is around 80.5 %.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.59-68
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• 2010

This study addresses the issue of whether Korean(L1)-English(L2) non-proficient bilinguals are affected by the native coda-neutralization process when recognizing words in English continuous speech. Korean phonological rules require that if liaison occurs between 'words', then coda-neutralization process must come before the liaison process, which results in liaison-consonants being coda-neutralized ones such as /b/, /d/, or /g/, rather than non-neutralized ones like /p/, /t/, /k/, /$t{\int}$/, /$d_{\Im}$/, or /s/. Consequently, if Korean listeners apply their native coda-neutralization rules to English speech input, word detection will be easier when coda-neutralized consonants precede target words than when non-neutralized ones do. Word-spotting and word-monitoring tasks were used in Experiment 1 and 2, respectively. In both experiments, listeners detected words faster and more accurately when vowel-initial target words were preceded by coda-neutralized consonants than when preceded by coda non-neutralized ones. The results show that Korean listeners exploit their native phonological process when processing English, irrespective of whether the native process is appropriate or not.

• MALSORI
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• no.46
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• pp.87-102
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• 2003

In this paper, we present an English pronunciation correction system for Korean speakers and show some of experimental results on it. The aim of the system is to detect mispronounced phonemes in spoken words and to give appropriate correction comments to users. There are several English pronunciation correction systems adopting speech recognition technology, however, most of them use conventional speech recognition engines. From this reason, they could not give phoneme based correction comments to users. In our system, we build two kinds of phoneme models: standard native speaker models and Korean's error models. We also design recognition network based on phonemes to detect Koreans' common mispronunciations. We get 90% detection rate in insertion/deletion/replacement of phonemes, but we cannot get high detection rate in diphthong split and accents.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.115-124
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• 2010

The current study investigated the role of acoustic correlates of domain-initial strengthening in lexical segmentation of a non-native language. In a series of cross-modal identity-priming experiments, native Korean listeners heard English auditory stimuli and made lexical decision to visual targets (i.e., written words). The auditory stimuli contained critical two word sequences which created temporal lexical ambiguity (e.g., 'mill#company', with the competitor 'milk'). There was either an IP boundary or a word boundary between the two words in the critical sequences. The initial CV of the second word (e.g., [$k_{\Lambda}$] in 'company') was spliced from another token of the sequence in IP- or Wd-initial positions. The prime words were postboundary words (e.g., company) in Experiment 1, and preboundary words (e.g., mill) in Experiment 2. In both experiments, Korean listeners showed priming effects only in IP contexts, indicating that they can make use of IP boundary cues of English in lexical segmentation of English. The acoustic correlates of domain-initial strengthening were also exploited by Korean listeners, but significant effects were found only for the segmentation of postboundary words. The results therefore indicate that L2 listeners can make use of prosodically driven phonetic detail in lexical segmentation of L2, as long as the direction of those cues are similar in their L1 and L2. The exact use of the cues by Korean listeners was, however, different from that found with native English listeners in Cho, McQueen, and Cox (2007). The differential use of the prosodically driven phonetic cues by the native and non-native listeners are thus discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.519-524
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• 2021

Recently, the fourth industrial revolution has become an area of interest to many countries, mainly in major advanced countries. Artificial intelligence technology, the core technology of the fourth industrial revolution, is developing in a form of convergence in various fields and has a lot of influence on the edutech field to change education innovatively. This paper builds an experimental environment using the DTW speech recognition algorithm and deep learning on various native and non-native data. Furthermore, through comparisons with CNN algorithms, we study non-native speakers to correct them with similar pronunciation to native speakers by measuring the similarity of English pronunciation.