• Health Policy and Management
• /
• v.23 no.4
• /
• pp.415-426
• /
• 2013

Background: This study was aim to identify the specific words and to develop the scale for the public health center (PHC) image. Methods: We collected 824 words from the previous studies and by open questions and reduced them by 77 words, then which were rated properly by 355 citizens of Seoul. We examined explanatory factor analysis for 69 words, and examined content validity test and confirmatory factor analysis (CFA) for the image structures (4 factors and 16 words). And then we developed the image questionnaire using them through council. We conducted a survey and retested the PHC image scales as the measuring tool targeting 2,000 persons, and compared the inexperience and experience persons for PHC usage. Results: The image structures were consisted of 4 factors and 16 words such as 'trustworthiness' (warm, exemplary, faithful, service-mindedness, beneficial to health), 'fairness' (honesty, clear, consistent, ruled), 'development possibility' (changing, goal-directed, developmental, propulsive), and 'flexibility' (not authoritative, not perfunctory, not rigid) in total. Cronbach's ${\alpha}$ values of all factors were above 0.7. As a result of CFA, model fit indexes yielded satisfactory results (root mean square error of approximation [RMSEA] 0.049, goodness of fit index [GFI] 0.937, and adjusted goodness of fit index [AGFI] 0.912). According to the result of retest for measuring tool by using other samples, Cronbach's ${\alpha}$ values were above 0.8, and model fit indexes yielded satisfactory results (RMSEA 0.059, GFI 0.952, AGFI 0.933). RMSEAs of the inexperiences and the experiences were each 0.59, 0.68. Conclusion: A reliable, valid, and generalizable scale was created for PHC image.

• Phonetics and Speech Sciences
• /
• v.3 no.1
• /
• pp.3-14
• /
• 2011

It has been posited that in English, native listeners use the Metrical Segmentation Strategy (MSS) for the segmentation of continuous speech. Strong syllables tend to be perceived as potential word onsets for English native speakers, which is due to the high proportion of strong syllables word-initially in the English vocabulary. This study investigates whether Koreans employ the same strategy when segmenting speech input in English. Word-spotting experiments were conducted using vowel-initial and consonant-initial bisyllabic targets embedded in nonsense trisyllables in Experiment 1 and 2, respectively. The effect of strong syllable was significant in the RT (reaction times) analysis but not in the error analysis. In both experiments, Korean listeners detected words more slowly when the word-initial syllable is strong (stressed) than when it is weak (unstressed). However, the error analysis showed that there was no effect of initial stress in Experiment 1 and in the item (F2) analysis in Experiment 2. Only the subject (F1) analysis in Experiment 2 showed that the participants made more errors when the word starts with a strong syllable. These findings suggest that Koran listeners do not use the Metrical Segmentation Strategy for segmenting English speech. They do not treat strong syllables as word beginnings, but rather have difficulties recognizing words when the word starts with a strong syllable. These results are discussed in terms of intonational properties of Korean prosodic phrases which are found to serve as lexical segmentation cues in the Korean language.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.5
• /
• pp.1273-1280
• /
• 1996

In order perform high accuracy recognition of text recognition systems, the recognized text must be processed through a post-processing stage using contextual information. We present a system that combines multiple knowledge sources to post-process the output of an optical character recognition(OCR) system. The multiple knowledge sources include characteristics of word, wrongly recognized types of Hangul characters, and Hangul word learning In this paper, the wrongly recognized characters which are made by OCR systems are collected and analyzed. We imput a Korean dictionary with approximately 15 0,000 words, and Korean language texts of Korean elementary/middle/high school. We found that only 10.7% words in Korean language texts of Korean elementary/middle /high school were used in a Korean dictionary. And we classified error types of Korean character recognition with OCR systems. For Hangul word learning, we utilized indexes of texts. With these multiple knowledge sources, we could predict a proper word in large candidate words.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.1
• /
• pp.97-103
• /
• 2001

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. Although the ML method has good performance, it dose not take account into discrimination to other words. To complement this problem, a word verification method by re-recognition of the recognized word and its similar word using the discriminative function of the two words. To find the similar word, the probability of other words to the HMM is calculated and the word showing the highest probability is selected as the similar word of the mode. To achieve discrimination to each word the weight to each state is appended to the HMM parameter. The weight is calculated by genetic algorithm. The verificator complemented discrimination of each word and reduced the error occurred by similar word. As a result of verification the total error is reduced by about 22%

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.30A no.3
• /
• pp.16-33
• /
• 1993

New decoding algorithm of double-error-correction Reed-Solmon codes over GF(2$^{8}$) for optical compact disks is proposed and decoding algorithm of RS codes with triple-error-correcting capability is presented in this paper. First of all. efficient algorithms for estimating the number of errors in the received code words are presented. The most complex circuits in the RS decoder are parts for soving the error-location numbers from error-location polynomial, so the complexity of those circuits has a great influence on overall decoder complexity. One of the most known algorithm for searching the error-location number is Chien's method, in which all the elements of GF(2$^{m}$) are substituted into the error-location polynomial and the error-location number can be found as the elements satisfying the error-location polynomial. But Chien's scheme needs another 1 frame delay in the decoder, which reduces decoding speed as well as require more stroage circuits for the received ocode symbols. The ther is Polkinghorn method, in which the roots can be resolved directly by solving the error-location polynomial. Bur this method needs additional ROM (readonly memory) for storing tthe roots of the all possible coefficients of error-location polynomial or much more complex cicuit. Simple, efficient, and high speed method for solving the error-location number and decoding algorithm of double-error correction RS codes which reudce considerably the complexity of decoder are proposed by using Hilbert theorems in this paper. And the performance of the proposed decoding algorithm is compared with that of conventional decoding algorithms. As a result of comparison, the proposed decoding algorithm is superior to the conventional decoding algorithm with respect to decoding delay and decoder complexity. And decoding algorithm of RS codes with triple-error-correcting capability is presented, which is suitable for error-correction in digital audio tape, also.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.2
• /
• pp.27-34
• /
• 2001

Utterance verification is used in variable vocabulary word recognition to reject the word that does not belong to in-vocabulary word or does not belong to correctly recognized word. Utterance verification is an important technology to design a user-friendly speech recognition system. We propose a new utterance verification algorithm for no-training utterance verification system based on the minimum verification error. First, using PBW (Phonetically Balanced Words) DB (445 words), we create no-training anti-phoneme models which include many PLUs(Phoneme Like Units), so anti-phoneme models have the minimum verification error. Then, for OOV (Out-Of-Vocabulary) rejection, the phoneme-based confidence measure which uses the likelihood between phoneme model (null hypothesis) and anti-phoneme model (alternative hypothesis) is normalized by null hypothesis, so the phoneme-based confidence measure tends to be more robust to OOV rejection. And, the word-based confidence measure which uses the phoneme-based confidence measure has been shown to provide improved detection of near-misses in speech recognition as well as better discrimination between in-vocabularys and OOVs. Using our proposed anti-model and confidence measure, we achieve significant performance improvement; CA (Correctly Accept for In-Vocabulary) is about 89％, and CR (Correctly Reject for OOV) is about 90％, improving about 15-21％ in ERR (Error Reduction Rate).

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.83-92
• /
• 2011

Typographical errors by the author's mistyping occur frequently in a document being prepared with word processors contrary to usual publications. Preparing this online document, the most common orthographical errors are spelling errors resulting from incorrectly typing intent keys to near keys on keyboard. Typical spelling checkers detect and correct these errors by using morphological analyzer. In other words, the morphological analysis module of a speller tries to check well-formedness of input words, and then all words rejected by the analyzer are regarded as misspelled words. However, if morphological analyzer accepts even mistyped words, it treats them as correctly spelled words. In this paper, I propose a simple method capable of detecting and correcting errors that the previous methods can not detect. Proposed method is based on the characteristics that typographical errors are generally not repeated and so tend to have very low frequency. If words generated by operations of deletion, exchange, and transposition for each phoneme of a low frequency word are in the list of high frequency words, some of them are considered as correctly spelled words. Some heuristic rules are also presented to reduce the number of candidates. Proposed method is able to detect not syntactic errors but some semantic errors, and useful to scoring candidates.

• Phonetics and Speech Sciences
• /
• v.10 no.3
• /
• pp.31-39
• /
• 2018

This paper aims to present a taxonomy of non-standard words (NSW) for developing a French text normalization system and to propose a method for evaluating this system based on a corpus. The proposed taxonomy of French NSWs consists of 13 categories, including 2 types of letter-based categories and 9 types of number-based categories. In order to evaluate the text normalization system, a representative test set including NSWs from various text domains, such as news, literature, non-fiction, social-networking services (SNSs), and transcriptions, is constructed, and an evaluation equation is proposed reflecting the distribution of the NSW categories of the target domain to which the system is applied. The error rate of the test set is 1.64%, while the error rate of the whole corpus is 2.08%, reflecting the NSW distribution in the corpus. The results show that the literature and SNS domains are assessed as having higher error rates compared to the test set.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2001.04a
• /
• pp.439-442
• /
• 2001

The Accuracy of a free-form artifact is affected by machine tool errors, machining process errors, environmental causes and other uncertainty. This paper deals with methodological approach about machine tool errors that are defined the relationship between CMM and OMM inspections of the free-form artifact. In order to analyze the measurement data, Reverse engineering was used. In other words, Surface of Free-Form Artifact is generated by NURBS surface approximation method. Finally, Volumetric error map is made to compare surface of CMM data with that of OMM data.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.1E
• /
• pp.64-70
• /
• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. But those models suffer from poor discrimination between acoustically similar words. In this paper we propose an discriminative training algorithm for predictive neural network models. This algorithm is derived from GPD (Generalized Probabilistic Descent) algorithm coupled with MCEF(Minimum Classification Error Formulation). It allows direct minimization of a recognition error rate. Evaluation of our training algoritym on ten Korean digits shows its effectiveness by 30% reduction of recognition error.