• MALSORI
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• no.51
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• pp.39-55
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• 2004

Working memory is the term used to refer to the mental workplace in which information can be temporarily stored and manipulated during complex everyday activities such as understanding language. The studies on language and working memory are based on Baddeley's phonological working memory and Daneman and Carpenter's functional working memory. This article reviews two working memory models and the studies on language and working memory based on each model. These are described in the implication of working memory in language development and specific language impairment-evaluation and treatment.

• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.95-102
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• 2011

The purpose of this study was to identify the characteristics of the phonological discrimination ability and phonological working memory of 10 typically developing children aged 4, and 10 other children with Specific Language Impairments whose language age is similar. In orders to compare their phonological discrimination ability among phonological awareness, discrimination tasks were conducted at the syllable and phoneme levels. Also, in order to compare their phonological working memory, the subjects repeated nonsense syllables. The research results may be summarized as follows: First, the children with Specific Language Impairments demonstrated a lower performance than the typically developing children in phonological discrimination ability at both syllable and phoneme levels, and the difference between the groups was statistically significant. Second, the children with Specific Language Impairments exhibited a lower phonological working memory performance in all syllables compared with normal children. Although there was no significant difference in 2 and 3 syllables, a significant difference appeared as the length of the syllables became longer from 4 to 6 syllables. It is deemed necessary to conduct research into qualitative and quantitative differences through an formal assessment of the phonological awareness and phonological working memory of children with Specific Language Impairments.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.201-203
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• 2005

The best method to study language is to talking with a native speaker. A voice recognition technology can be used to develope a language study machine. SD(Speaker dependant) and SI(speaker independant) voice recognition method is used for the language study machine. MP3 Player. FM Radio. Alarm clock functions are added to enhance the value of the product. The machine is designed with a DSP(Digital Signal Processing) chip for voice recognition. MP3 encoder/decoder chip. FM tumer and SD flash memory card. This paper deals with the application of SD ad SD voice recognition. flash memory file system. PC download function using USB ports, English conversation text function by the use of SD flash memory. LCD display control. MP3 encoding and decoding, etc. The study contents are saved in SD flash memory. This machine can be helpful from child to adult by changing the SD flash memory.

• Speech Sciences
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• v.8 no.4
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• pp.119-129
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• 2001

Language is one aspect of cognition, along with attention and concentration, learning and memory, visuospatial abilities, and executive function. The purpose of this study was to determine the effect of language intervention by cognitive approach on language expressive performance in a patient with conduction aphasia. This study used several tasks such as Attention and concentration task, visual memory tasks, memory tasks, categorization, divergent thinking, self-monitoring and evaluate thinking. The effects of treatment were evaluated by periodic probing of both trained and untrained familiar words in three tasks; picture naming, answering to questions and telling stories. The results showed improvements both in trained and untrained words. Therefore, we concluded that expressive language performance of this aphasic patient is amenable to this intervention, and that cognitive therapy approach can be useful.

• Korean Journal of Cognitive Science
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• v.23 no.4
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• pp.425-455
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• 2012

Multi-dimensional verbal short-term memory mechanisms are largely divided into the phonological channel and the lexical-semantic channel. The former is called phonological short-term memory and the latter is called semantic short-term memory. Phonological short-term memory is further segmented into the phonological input buffer and the phonological output buffer. In this study, the language performance of each of three patients with similar levels of conduction aphasia was analyzed in terms of multi-dimensional verbal short-term memory. To this end, three patients with conduction aphasia were instructed to perform four different aspects of language tasks that are spontaneous speaking, repetition, spontaneous writing, and dictation in both word and sentence level. Moreover, the patients' phonological memories and semantic short-term memories were evaluated using digit span tests and verbal learning tests. As a result, the three subjects exhibited various types of performances and error responses in the four aspects of language tests, and the short-term memory tests also did not produce identical results. The language performance of three patients with conduction aphasia can be explained according to whether the defects occurred in the semantic short-term memory, phonological input buffer and/or phonological output buffer. In this study, the relations between language and multi-dimensional verbal short-term memory were discussed based on the results of language tests and short-term memory tests in patients with conduction aphasia.

• International Journal of Knowledge Content Development & Technology
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• v.5 no.2
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• pp.75-101
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• 2015

In this paper, we investigate the notion of error correction memory applied to technical texts. The main purpose is to introduce flexibility and context sensitivity in the detection and the correction of errors related to Constrained Natural Language (CNL) principles. This is realized by enhancing error detection paired with relatively generic correction patterns and contextual correction recommendations. Patterns are induced from previous corrections made by technical writers for a given type of text. The impact of such an error correction memory is also investigated from the point of view of the technical writer's cognitive activity. The notion of error correction memory is developed within the framework of the LELIE project an experiment is carried out on the case of fuzzy lexical items and negation, which are both major problems in technical writing. Language processing and knowledge representation aspects are developed together with evaluation directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.837-852
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• 2021

Recurrent neural network (RNN) architectures have been used for language modeling (LM) tasks that require learning long-range word or character sequences. However, the RNN architecture is still suffered from unstable gradients on long-range sequences. To address the issue of long-range sequences, an attention mechanism has been used, showing state-of-the-art (SOTA) performance in all LM tasks. A differentiable neural computer (DNC) is a deep learning architecture using an attention mechanism. The DNC architecture is a neural network augmented with a content-addressable external memory. However, in the write operation, some information unrelated to the input word remains in memory. Moreover, DNCs have been found to perform poorly with low numbers of weight parameters. Therefore, we propose a robust memory deallocation method using a limited retention vector. The limited retention vector determines whether the network increases or decreases its usage of information in external memory according to a threshold. We experimentally evaluate the robustness of a DNC implementing the proposed approach according to the size of the controller and external memory on the enwik8 LM task. When we decreased the number of weight parameters by 32.47%, the proposed DNC showed a low bits-per-character (BPC) degradation of 4.30%, demonstrating the effectiveness of our approach in language modeling tasks.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.45-50
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• 2020

A Language model with neural networks commonly trained with likelihood loss. Such that the model can learn the sequence of human text. State-of-the-art results achieved in various language generation tasks, e.g., text summarization, dialogue response generation, and text generation, by utilizing the language model's next token output probabilities. Monotonous and boring outputs are a well-known problem of this model, yet only a few solutions proposed to address this problem. Several decoding techniques proposed to suppress repetitive tokens. Unlikelihood training approached this problem by penalizing candidate tokens probabilities if the tokens already seen in previous steps. While the method successfully showed a less repetitive generated token, the method has a large memory consumption because of the training need a big vocabulary size. We effectively reduced memory footprint by encoding words as sequences of subword units. Finally, we report competitive results with token level unlikelihood training in several automatic evaluations compared to the previous work.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.167-172
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• 2019

The purpose of this study was to investigate the effect of computer based cognitive rehabilitation program on the generative naming. Twenty - one patients were assigned to the CoTras program and eight were treated with traditional face - to - face language rehabilitation such as paper and table activities. The experimental group and the control group performed sequential language recall memory training, association memory recall training, language categorization memory training, and language integrated memory training for 12 weeks. The Welch's robust ANCOVA showed significant differences in mean fluency and MMSE-K changes (p<0.05). On the other hand, phonemic fluency increased significantly after 12 weeks of treatment compared to baseline in both experimental and control groups, but there was no statistically significant difference between treatment groups. The results of this study suggest that the computer based cognitive rehabilitation program may be more effective in improving the semantic fluency than the conventional cognitive-linguistic rehabilitation.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.31-34
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• 2018

대화 시스템에서 사람과 기계와의 모든 발화에서 발생하는 상황들을 모두 규칙화할 수 없기 때문에 자연스러운 대화가 단절되는 breakdown 현상이 빈번하게 일어날 수 있다. 이런 현상이 발생하는 이유는 다음과 같다. 첫째, 대화에서는 다양한 도메인이 등장하기 때문에 시스템이 커버할 수 있는 리소스가 부족하며, 둘째, 대화 데이터에서 학습을 위한 annotation되어 있는 많은 양의 코퍼스를 보유하기에는 한계가 있으며, 모델에 모든 대화 흐름의 히스토리를 반영하기 어렵다. 이런 한계점이 존재함에도 breakdown detection은 자연스러운 대화 시스템을 위해서는 필수적인 기능이다. 본 논문은 이런 이슈들을 해소하기 위해서 memory attention기반의 새로운 모델을 제안하였다. 제안한 모델은 대화내에 발화에 대해 memory attention을 이용하여 과거 히스토리가 반영되기 때문에 자연스러운 대화흐름을 잘 detection할 수 있으며, 기존 모델과의 성능비교에서 state-of-the art 결과를 도출하였다.