• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.595-617
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• 2010

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner's cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.43-51
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• 2004

The convergence of mobile communications and handheld computers offers the opportunity to develop technology that will assist individuals and groups to learn anytime, anywhere. In this thesis, we suggest a learning model for educational mobile contents, and implement a mobile contents for English vocabulary learning. The mobile contents consist of two parts: vocabulary learning and vocabulary evaluation. The vocabulary learning provides the mobile learner not only the display of English vocabulary but also the pronunciation of native speaker. The degree of learning achievement is evaluated by the quiz program of game style. Accordingly, the proposed prototype system for English vocabulary learning is designed and implemented, as an exemplar of personal mobile systems for edutainment mobile learning.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.89-93
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• 2006

This paper presents ETRI broadcast news speech recognition system. There are two major issues on the broadcast news speech recognition: 1) real-time processing and 2) out-of-vocabulary handling. For real-time processing, we devised the dual decoder architecture. The input speech signal is segmented based on the long-pause between utterances, and each decoder processes the speech segment alternatively. One decoder can start to recognize the current speech segment without waiting for the other decoder to recognize the previous speech segment completely. Thus, the processing delay is not accumulated. For out-of-vocabulary handling, we updated both the vocabulary and the language model, based on the recent news articles on the internet. By updating the language model as well as the vocabulary, we can improve the performance up to 17.2% ERR.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.2
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• pp.37-42
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• 2008

In this paper, the vocabulary-independent continuous speech recognition system for speech control of intelligent home-network is presented. This study suggests a conversational scenario of continuous natural vocabulary based upon keywords for recognition on natural speech command, and a way of optimizing the recognition system by constructing a recognition system and database based upon keywords.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.83-84
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• 2011

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.165-172
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• 2010

There are two method of error correction in vocabulary recognition system. one error pattern matting base on method other vocabulary mean pattern base on method. They are a failure while semantic of key-word problem for error correction. In improving, in this paper is propose system of key-word error correction using algorithm of syllable restoration. System of key-word error correction by processing of semantic parse through recognized phoneme meaning. It's performed restore by algorithm of syllable restoration phoneme apply fluctuation before word. It's definitely parse of key-word and reduced of unrecognized. Find out error correction rate using phoneme likelihood and confidence for system parse. When vocabulary recognition perform error correction for error proved vocabulary. system performance comparison as a result of recognition improve represent 2.3% by method using error pattern learning and error pattern matting, vocabulary mean pattern base on method.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1471-1479
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• 2008

In this paper, we propose a technique to improve Out-Of-Vocabulary(OOV) rejection algorithms in variable vocabulary recognition system which is much used in ASR(Automatic Speech Recognition). The rejection system can be classified into two categories by their implementation method, keyword spotting method and utterance verification method. The utterance verification method uses the likelihood ratio of each phoneme Viterbi score relative to anti-phoneme score for deciding OOV. In this paper, we add speaker verification system before utterance verification and calculate an speaker verification probability. The obtained speaker verification probability is applied for determining the proposed variable-confidence threshold. Using the proposed method, we achieve the significant performance improvement; CA(Correctly Accepted for keyword) 94.23%, CR(Correctly Rejected for out-of-vocabulary) 95.11% in office environment, and CA 91.14%, CR 92.74% in noisy environment.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.496-503
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• 2010

Speech recognition system is developed of standalone, In case of a mobile terminal using that low recognition rate represent because of limitation of memory size and audio compression. This study suggest vocabulary recognition highest performance improvement system for separate acoustic search and lexical search. Acoustic search is carry out in mobile terminal, lexical search is carry out in server processing system. feature vector of speech signal extract using GMM a phoneme execution, recognition a phoneme list transmission server using Lexical Tree Search algorithm lexical search recognition execution. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.71%, represent recognition speed of 1.58 second.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.127-134
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• 2010

In continuous vocabulary recognition system by probability distribution of clustering method has used model parameters of an advance estimate to generated each contexts for phoneme data surely needed but it has it's bad points of gaussian model the accuracy unsecure of composed model for phoneme data. To improve suggested probability distribution mixed gaussian model to optimized that phoneme data search supported configuration thread system. This paper of configuration thread system has used extension facet classification user phoneme configuration thread information offered gaussian model the accuracy secure. System performance as a result of represent vocabulary dependence recognition rate of 98.31%, vocabulary independence recognition rate of 97.63%.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.41-44
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• 2002

This paper is related to the enhancement of speech recognition rate using enhanced pronunciation dictionary. Modern large vocabulary, continuous speech recognition systems have pronunciation dictionaries. A pronunciation dictionary provides pronunciation information for each word in the vocabulary in phonemic units, which are modeled in detail by the acoustic models. But in most speech recognition system based on Hidden Markov Model, actual pronunciation variations are disregarded. Without the pronunciation variations in the speech recognition system, the phonetic transcriptions in the dictionary do not match the actual occurrences in the database. In this paper, we proposed the unvoiced rule of semivowel in allophone rules to pronunciation dictionary. Experimental results on speech recognition system give higher performance than existing pronunciation dictionaries.