• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.274-284
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• 2002

Word segmentation errors occurring in text preprocessing often insert incorrect words into recognition vocabulary and cause poor language models for Korean large vocabulary continuous speech recognition. We propose an automatic word segmentation algorithm using Markov chains and syllable-based n-gram language models in order to correct word segmentation error in teat corpora. We assume that a sentence is generated from a Markov chain. Spaces and non-space characters are generated on self-transitions and other transitions of the Markov chain, respectively Then word segmentation of the sentence is obtained by finding the maximum likelihood path using syllable n-gram scores. In experimental results, the algorithm showed 91.58% word accuracy and 96.69% syllable accuracy for word segmentation of 254 sentence newspaper columns without any spaces. The algorithm improved the word accuracy from 91.00% to 96.27% for word segmentation correction at line breaks and yielded the decomposition accuracy of 96.22% for compound-noun decomposition.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.101-113
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• 2020

This study aimed to explore how effective explicit form-focused instruction (FFI) is in teaching the schwa vowel /ə/ to EFL students in a classroom setting. The participants were 25 female high school students, who were divided into the experimental group (n=13) and the control group (n=12). One female American also participated in the study for a speech sample as a reference. The treatment, which involves shadowing model pronunciation by the researcher and a free text-to-speech software and the researcher's feedback in a private session, was given to the control group over a month and a half. The speech samples, for which the participants read the 14 polysyllabic stimulus words followed by the sentences containing the words, were collected before and after the treatment. The paired-samples t test and non-parametric Wilcoxon signed-rank test were used for analysis. The results showed that the participants of the experimental group in the post-test reduced the duration of the schwa by around 40 percent compared to the pre-test. However, little effect was found in approximating the participants' distribution patterns of /ə/ measured by the F1/F2 formant frequencies to the reference point, which was 539 Hz (F1) by 1797 Hz (F2). The findings of this study suggest that explicit FFI with multiple repetitions and corrective feedback is partly effective in teaching pronunciation.

• Language and Information
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• v.17 no.2
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• pp.93-121
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• 2013

Identifying the speakers of individual utterances mentioned in textual stories is an important step towards developing applications that involve the use of unique characteristics of speakers in stories, such as robot storytelling and story-to-scene generation. Despite the usefulness, it is a challenging task because not only human entities but also animals and even inanimate objects can become speakers especially in fairytales so that the number of candidates is much more than that in other types of text. In addition, since the action of speaking is not always mentioned explicitly, it is necessary to infer the speaker from the implicitly mentioned speaking behaviors such as appearances or emotional expressions. In this paper, we investigate a method to exploit linguistic clues to identify the speakers of utterances from textual fairytale stories in Korean, especially in order to handle such challenging issues. Compared with the previous work, the present work takes into account additional linguistic features such as vocative roles and pairs of conversation participants, and proposes the use of discourse-level turn-taking behaviors between speakers to further reduce the number of possible candidate speakers. We describe a simple rule-based method to choose a speaker from candidates based on such linguistic features and turn-taking behaviors.

• Research in Mathematical Education
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• v.4 no.1
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• pp.45-55
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• 2000

The rapid proliferation of wireless networks provides a ubiquitous channel for delivering instructional materials at the convenience of the user. By delivering content through portable devices linked to the Internet, the full spectrum of multimedia capabilities is available for engaging the user's interest. This capability encompasses not only text but images, video, speech generation and voice recognition. Moreover, the incorporation of machine learning capabilities at the source provides the ability to tailor the material to the general level of expertise of the user as well as the immediate needs of the moment: for instance, a request for information regarding a particular city might be covered by a leisurely presentation if solicited from the home, but more tersely if the user happens to be driving a car. This paper presents system architecture to support mobile instruction in conjunction with knowledge-based tutoring capabilities. For concreteress, the general concepts are examined in the context of a system for mathematics education on the Web.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.688-702
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• 1998

In this paper, we propose sign language morpheme generation rule corresponding to morpheme analysis for each part of speech. Korean natural sign language has extremely limited vocabulary, and the number of grammatical components eing currently used are limited, too. In this paper, therefore, we define natural sign language grammar corresponding to Korean language grammar in order to translate natural Korean language sentences to the corresponding sign language. Each phrase should define sign language morpheme generation grammar which is different from Korean language analysis grammar. Then, this grammar is applied to morpheme analysis/combination rule and sentence structure analysis rule. It will make us generate most natural sign language by definition of this grammar.

• Language and Information
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• v.8 no.1
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• pp.117-136
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• 2004

In this paper, we have proposed Auto- TSS, an automatic transcription module of three ambiguous symbols-period (．), colon (:) and slash (／)--using their linguistic contexts. Few previous studies have discussed the problems of ambiguities in reading those symbols into Korean alphabetic letters in order to improve the current Korean TTS (Text-To-Speech) systems. We have classified 9 different reading formulae of the three symbols, analyzed their left and right contexts, and investigated selection rules and distributions between the symbols and their contexts. Based on these linguistic features, 30 stereotyped patterns, 53 rules and 5 heuristics determining the types of reading formulae are investigated for Auto-TSS. This module works modularly in 4 steps. The pilot test was conducted with three test suites, which contain respectively 6,979, 3,491 and 2,450 morpheme clusters containing at least one of three ambiguous symbols and Arabic numeral(s). Encouraging results of 94.3%, 93.0%, 94.2% accuracy were obtained for the test suites. Our next phases are to develop a guessing routine for unknown contexts of the union symbols by using statistical information; to refine the proper nouns and terminology detecting module; and to apply Auto-TSS on a larger scale.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.59-72
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• 2022

This text discusses elements and characteristics of contemporary informational reality, that is, the ways of producing, circulating, organizing, using, and appropriating information in the current context. Initially, seven terms and concepts used to describe this reality are discussed: fake news, false testimonials, hate speech, scientific negationism, disinformation, post-truth, and infodemic. Next, an attempt is made to present a framework for such phenomena as an object of study in information science. Therefore, this scenario is characterized based on the three main models of information science study: physical, cognitive, and social. The contribution of each of them to the study of contemporary informational reality is analyzed, identifying aspects such as the bubble effect, clickbaits, confirmation bias, cults of amateurism, and post-truth culture. Finally, it presents the discussion of a possible veritistic turn in the field, in order to think about elements not covered so far by information science in its task and challenge of producing adequate understanding and diagnoses of current phenomena. In conclusion, it is argued that only accurate and comprehensive diagnoses of such phenomena will allow information science to develop services and systems capable of combating their harmful effects.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.24-29
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• 2023

In this paper, we propose a real-time bus arrival notification system using deep learning to guarantee movement rights for the visually impaired. In modern society, by using location information of public transportation, users can quickly obtain information about public transportation and use public transportation easily. However, since the existing public transportation information system is a visual system, the visually impaired cannot use it. In Korea, various laws have been amended since the 'Act on the Promotion of Transportation for the Vulnerable' was enacted in June 2012 as the Act on the Movement Rights of the Blind, but the visually impaired are experiencing inconvenience in using public transportation. In particular, from the standpoint of the visually impaired, it is impossible to determine whether the bus is coming soon, is coming now, or has already arrived with the current system. In this paper, we use deep learning technology to learn bus numbers and identify upcoming bus numbers. Finally, we propose a method to notify the visually impaired by voice that the bus is coming by using TTS technology.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.255-273
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• 2019

Recently, the table of contents (TOC) has been becoming increasingly accessible and utilized. The study conducted descriptive statistics and comparative analysis of the table of contents in terms of parts of speech and subject in text. For this purpose, this study chose the books of the social sciences field from acquisition lists of an academic library, obtained Dewey class numbers of target books from KERIS union catalog, and extracted TOC data from online bookstore. Morphological analysis was performed on each book titles and TOCs, and descriptive statistics and frequency analysis were carried out. As a result, nouns made up roughly half of the morphemes of titles or the TOCs. TOCs had about 50 times more nouns than titles. The percentage of unique nouns that appeared only in the table of contents is estimated to be 95.2% of the TOC's total nouns. The table of contents also showed a differences in its lengths depending on the field of social science.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.109-118
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• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.