• Journal of Distribution Science
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• v.17 no.7
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• pp.27-38
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• 2019

Purpose - This study analyzes effects of word of mouth intention of restaurant product via SNS on trust of products, purchase intention, and word of mouth intention targeting restaurant customers. Research design, data, and methodology - Targeting restaurant customers, 500 surveys were distributed in restaurants located in Seoul (where restaurants are clustered, such as Myeongdong, Dongdaemoon Station Shopping Center, and Sadangdong) between July 1st, 2016 and July 30th, 2016. Among those, 490 were collected, and 478 were used for analysis excluding those with no answer or insincere answers. Results - SNS word of mouth information characteristics had significant effects on trust of restaurant product in the order of vividness, neutrality, and timing. Trust on restaurant product has significant effect on perceived risks (-) and perceived benefits (+) by SNS. While perceived benefits of restaurant product on SNS has effect on purchase intention, perceived risks of restaurant product on SNS does not affect purchase intention. Perceived benefits of restaurant product on SNS has significant effect on word of mouth intention, whereas perceived risks of restaurant product on SNS does not have significant effect on word of mouth intention. Conclusions - As marketing through SNS can bring about a huge reduction effect in terms of marking cost, it can be utilized as an effective promotion by not only large restaurant corporations, but also small restaurants.

• MALSORI
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• no.59
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.45-53
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• 2015

The current study investigated CVCC syllables in spontaneous American English speech to find out whether such syllables are produced as phonological units with a string of segments, showing a hierarchical structure. Transcribed data from the Buckeye Speech Corpus was used for the analysis in this study. The result of the current study showed that the constituents within a CVCC syllable as a phonological unit may have phonetic variations (namely, the final coda may undergo deletion). First, voiceless alveolar stops were the most frequently deleted when they occurred as the second final coda consonants of a CVCC syllable; this deletion may be an intermediate process on the way from the abstract form CVCC (with the rime VCC) to the actual pronunciation CVC (with the rime VC), a production strategy employed by some individual speakers. Second, in the internal structure of the rime, the proportion of deletion of the final coda consonant depended on the frequency of the word rather than on the position of postvocalic consonants on the sonority hierarchy. Finally, the segment following the consonant cluster proved to have an effect on the reduction of that cluster; more precisely, the following contrast was observed between obstruents and non-obstruents, reflecting the effect of sonority: when the segment following the consonant cluster was an obstruent, the proportion of deletion of the final coda consonant was increased. Among these results, the effect of word frequency played a critical role for promoting the deletion of the second coda consonant for clusters in CVCC syllables in spontaneous speech. The current study implies that the structure of syllables as phonological units can vary depending on individual speakers' lexical representation.

• Phonetics and Speech Sciences
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• v.15 no.1
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• pp.9-17
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• 2023

The purpose of this study is to determine how the Korean diphthong /jo/ shows phonetic variation in various linguistic environments. The pronunciation of /jo/ is discussed, focusing on the relationship between phonetic variation and the distribution range of vowels. The location in a word (monosyllable, word-initial, word-medial, word-final) and word class (content word, function word) were analyzed using the speech of 10 female speakers of the Seoul Corpus. As a result of determining the frequency of appearance of /jo/ in each environment, the pronunciation type and word class were affected by the location in a word. Frequent phonetic reduction was observed in the function word /jo/ in the acoustic analysis. The word class did not change the average phonetic values of /jo/, but changed the distribution of individual tokens. These results indicate that the linguistic environment affects the phonetic distribution of vowels.

• MALSORI
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• no.51
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• pp.71-84
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• 2004

In this paper, we propose the automatic speech database verification method(or called automatic verification) based on confidence measure for a large speech database. This method verifies the consistency between given transcription and speech using the confidence measure. The automatic verification process consists of two stages : the word-level likelihood computation stage and multi-level likelihood ratio computation stage. In the word-level likelihood computation stage, we calculate the word-level likelihood using the viterbi decoding algorithm and make the segment information. In the multi-level likelihood ratio computation stage, we calculate the word-level and the phone-level likelihood ratio based on confidence measure with anti-phone model. By automatic verification, we have achieved about 61% error reduction. And also we can reduce the verification time from 1 month in manual to 1-2 days in automatic.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.65-70
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• 1998

In this paper, we will to check and to correct mis-recognized word using Eojeol information. First, we divided into 16 classes that constituents in a Eojeol after we analyzed Korean statement into Eojeol units. Eojeol-Constituent state diagram constructed these constitutents, find the Left-Right Connectivity Information. As analogized the speech of connectivity information, reduced the number of cadidate words and restricted case of morphological analysis for mis-recognition Eojeol. Then, we improved correction speed uisng heuristic information as the adjacency information for Eojeol each other. In the correction phase, construct Reverse-Order Word Dictionary. Using this, we can trace word dictionary regardless of mis-recongnition word position. Its results show that improvement of recognition rate from 97.03% to 98.02% and check rate, reduction of chadidata words and morpholgical analysis cases.

• Speech Sciences
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• v.10 no.2
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• pp.15-25
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• 2003

In this paper, we propose an algorithm on OOV(Out-of-Vocabulary) rejection based on two-stage method. In the first stage, the algorithm rejects OOVs using generic word model, and then in the second stage, for further reduction of false acceptance, it rejects words which have low similarity to the candidate by measuring the distance between HMM models. For the experiment, we choose 20 in-vocabulary words out of PBW445 DB distributed by ETRI. In case that the first stage is processed only, the false acceptance is 3% with 100% correct acceptance, and in case both stages are processed, the false acceptance is reduced to 1% with 100% correct acceptance.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• Korean Journal of Human Ecology
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• v.19 no.4
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• pp.675-685
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• 2010

This study analyzed risk perception and risk reduction behaviors of male and female college students in their twenties who purchased fashion products in internet shopping malls. It also investigated the relationship between risk perception and risk reduction behavior as well as the ways in which groups, categorized by risk perception, differed in their risk reduction behaviors. The results of this study were as follows: first, seven factors of risk perception were identified. These were product quality, shipping, product image, payment, economic feasibility, fear of other people's reactions, and size. Six types of risk reduction behavior were also identified. These were product comparison, word-of-mouth information search, price search, preference for name-brand, service comparison, and referring to experiences. Next, a correlational analysis of the factors of risk perception and those of risk reduction behavior showed several patterns. The highest positive correlation was between economic risk perception and product comparison behavior. In addition, shipping risk perception was positively correlated with service comparison behavior and product quality and product image had a positive correlation with word-of-mouth information search behavior. Third, customers of internet shopping malls could be categorized into three groups: shipping risk perception group, high risk perception group, and product quality risk perception group. The groups were shown by factor analysis to be significantly different to each other. Finally, risk reduction behavior was investigated according to the different groups of risk perception of the internet shopping malls and the results showed significant differences among groups.

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