• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.77-87
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• 2019

The study aimed to develop Speech Range Profile (SRP) and to examine and validate its clinical application. Forty-five participants without voice disorders aged 18-29 years were compared using SRP and Voice Range Profile (VRP). The authors developed the "Fire!" paragraph as a SRP task compromising 14 sentences including all Korean spoken phonemes and sentence types. To compare SRP and VRP results, the participants read the paragraph (reading) and counted from 21 to 30 (counting) as a part of SRP tasks, and produced a vowel /a/ from low to high frequencies (gliding) and a shortened form of the VRP as a part of VRP tasks. $F0_{max}$, $F0_{min}$, $F0_{range}$, $I_{max}$, $I_{min}$, and $I_{range}$ for each task were measured and compared, showing that $F0_{max}$, $F0_{min}$, $F0_{range}$, $I_{max}$, and $I_{range}$ were not different between reading and gliding. $I_{min}$, had the lowest value in counting. It is concluded that the newly developed SRP task, reading the "Fire" paragraph, can yield a maximum phonation range similar to that found by VRP. Therefore, it is expected that voice evaluation can be effectively performed in a relatively short time by applying SRP with the "Fire" paragraph, a functional utterance task, in place of VRP, which may be difficult to measure long term or in cases of severe voice disorders.

• Journal of Korean Neurosurgical Society
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• v.45 no.4
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• pp.219-223
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• 2009

Objective : Functional magnetic resonance imaging (fMRI) is frequently used to localize language areas in a non-invasive manner. Various paradigms for presurgical localization of language areas have been developed, but a systematic quantitative evaluation of the efficiency of those paradigms has not been performed. In the present study, the authors analyzed different language paradigms to see which paradigm is most efficient in localizing frontal language areas. Methods : Five men and five women with no neurological deficits participated (mean age, 24 years) in this study. All volunteers were right-handed. Each subject performed 4 tasks, including fixation (Fix), sentence reading (SRI. pseudoword reading (PR), and word generation (WG). Fixation and pseudoword reading were used as contrasts. The functional area was defined as the area(s) with a t-value of more than 3.92 in fMRI with different tasks. To apply an anatomical constraint, we used a brain atlas mapping system, which is available in AFNI, to define the anatomical frontal language area. The numbers of voxels in overlapped area between anatomical and functional area were individually counted in the frontal expressive language area. Results : Of the various combinations, the word generation task was most effective in delineating the frontal expressive language area when fixation was used as a contrast (p<0.05). The sensitivity of this test for localizing Broca's area was 81 % and specificity was 70%. Conclusion : Word generation versus fixation could effectively and reliably delineate the frontal language area. A customized effective paradigm should be analyzed in order to evaluate various language functions.

• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.49-55
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• 2019

This study sought to investigate whether mean speaking fundamental frequency (SFF) can be predicted by parameters of voice and speech range profile (VRP and SRP) in Korean normal adults. Moreover, it explored whether gender differences exist in the absolute differences between the SFF and estimated SFF (ESFF) predicted by the VRP and SRP. A total of 85 native Korean speakers with normal voice participated in the study. Each participant was asked to perform the VRP task using the vowel /a/ and the SRP task using the first sentence of a Korean standard passage "Ga-eul". In addition, the SFF was measured with electroglottography during a passage reading task. Predictive factors of the SFF were explored and the absolute difference between the SFF and the ESFF (DSFF) was compared between gender groups. Results indicated that predictive factors were age, gender, minimum pitch and pitch range for the VRP (adjusted $R^2=.931$), and pitch range (in semi-tones) and maximum pitch for the SRP (adjusted $R^2=.963$), respectively. The SFF and ESFF predicted by the VRP and SRP showed a strong positive correlation. The DSFF of the VRP and SRP, as well as their sum did not differ by gender. In conclusion, the SFF during a passage reading task could be successfully predicted by the parameters of the VRP and SRP tasks. In further studies, clinical implications need to be explored in patients who may exhibit deviations in SFF.

• Phonetics and Speech Sciences
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• v.12 no.1
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• pp.19-31
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• 2020

The purpose of this study is to observe the realization of pause and breath groups from adult speakers and to examine how gender, generation, and tasks can affect this realization. For this purpose, we analyzed forty-eight male or female speakers. Their generation was divided into two groups: young, old. Task and gender affected both the realization of pause and breath groups. The length of the pause groups was longer in the read speech than in the spontaneous speech and female speech. On the other hand, the length of the breath group was longer in the spontaneous speech and the male speech. In the spontaneous speech, which requires planning, the speaker produced shorter length of pause group. The short sentence length of the reading material influenced the reason for which the length of the breath group was shorter in the reading speech. Gender difference resulted from difference in pause patterns between genders. In the case of the breath groups, the male speaker produced longer duration of pause than the female speaker did, which may be due to difference in lung capacity between genders. On the other hand, generation did not affect either the pause groups or the breath groups. The generation factor only influenced the number of syllables and the eojeols, which can be interpreted as the result of the difference in speech rate between generations.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.103-108
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• 2016

In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, ${\frac{{\theta}\;power}{mid\;{\beta}\;power}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2000.05a
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• pp.261-266
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• 2000

글을 이해하기 위해서는 어휘와 어휘간의 연결 및 전체 구조를 아는 것이 필요하다. 이는 비단 한국어뿐만 아니라 영어나 기타 다른 외국어에서도 마찬가지일 것이다. 본고는 두 가지를 고찰하기 위해 진행되었는데 우선 외국어로서 영어를 처리하는데 발생하는 구문적 중의성을 해결하는데 Garden Path Sentence(GPS), Late Closure(LC), PP의 세 문형에 따라 어떻게 해결하는지 알아보기 위한 것이다. 그리고 각 문형의 중의적 어절에서의 반응과 애매성 해소 어절에서의 반응에 따라 sysntactic module이 작용하는 것인지 알아보고자 한다. 예를 들어 "The boat floated down the streams sank"란 Garden Path 문장이 제시된 경우에 독자는 "sank"란 어휘가 제시되기 전까지 "floated"를 동사로 생각하게 되나 다음에 본동사인 "sank"가 제시될 경우 문장의 해석에 혼란을 갖게 될 것이다. 예문에서 "floated"가 문장에서 어떤 역할을 하는지 결정하는 것은 "sank"를 보고서야 가능하다. 이런 구문적 중의성을 해결하는 방식을 알아보기 위해 어절 단위로 제시된 자극을 읽는 자기 조절 읽기 과제(self-paced reading task)를 사용하였다. 각 어절을 읽는데 걸리는 시간을 측정한 실험 결과 GPS, PP, LC 모두 중의성을 지닌 영역이 중의성을 해소한 후와 각각 유형적으로 큰 차이가 없는 것으로 나타났다. 다만 GPS, CGPS, PP와 CPP는 어절 후반으로 갈수록 반응시간이 짧아졌다. 이는 우리나라 사람의 경우 외국어인 영어의 구문 중의성 해소는 구문 분석 단원(syntactic module)에 의한 자동적 처리라기보다 의미를 고려해 가면서 문법지식을 이용해 추론을 통한 구문 분석이라 할 수 있다.에 의한 자동적 처리라기보다 의미를 고려해 가면서 문법지식을 이용해 추론을 통한 구문 분석이라 할 수 있다.많았다(P<0.05).조군인 Group 1에서보다 높은 수준으로 발현되었다. 하지만 $12.5{\;}\mu\textrm{g}/ml$의 T. denticola sonicated 추출물로 전처리한 Group 3에서는 IL-2와 IL-4의 수준이 유의성있게 억제되어 발현되었다 (p < 0.05). 이러한 결과를 통하여 T. denticola에서 추출된 면역억제 단백질이 Th1과 Th2의 cytokine 분비 기능을 억제하는 것으로 확인 되었으며 이 기전이 감염 근관에서 발견되는 T. denticola의 치수 및 치근단 질환에 대한 병인기전과 관련이 있는 것으로 사료된다.을 보였다. 본 실험 결과, $Depulpin^{\circledR}은{\;}Tempcanal^{\circledR}와{\;}Vitapex^{\circledR}$에 비해 높은 세포 독성을 보여주공 있으나, 좀 더 많은 임상적 검증이 필요할 것으로 사료된다.중요한 역할을 하는 것으로 추론할 수 있다.근관벽을 처리하는 것이 필요하다고 사료된다.크기에 의존하며, 또한 이러한 영향은 $(Ti_{1-x}AI_{x})N$ 피막에 존재하는 AI의 함량이 높고, 초기에 증착된 막의 업자 크기가 작을 수록 클 것으로 여겨진다. 그리고 환경의 의미의 차이에 따라 경관의 미학적 평가가 달라진 것으로 나타났다.corner$적 의도에 의한 경관구성의 일면을 확인할수 있지만 엄밀히 생각하여 보면 이러한 예의 경우도 최락의 총체적인 외형은 마찬가지로 $\ulcorner$순응$\lrcorner$의 범위를 벗어나지 않는다. 그렇기 때문에도 $\ulcorner$순응$\lrcorner$과 $\ulcorner$표현$\lrcorner$의 성격과 형태를 외형상으로 더욱이 공간상에서는 뚜렷하게 경계

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.