• MALSORI
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• no.62
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• pp.51-68
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• 2007

Two experiments were conducted to determine the exact locus of the frequency effect in speech production. Experiment 1 addressed the question as to whether the word frequency effect arise from the stage of lemma selection. A picture-word interference task was performed to test the significance of interactions between the effects of target frequency, distractor frequency and semantic relatedness. There was a significant interaction between the distractor frequency and the semantic relatedness and between the target and the distractor frequency. Experiment 2 examined whether the word frequency effect is attributed to the lexeme level which represent phonological information of words. A methodological logic applied to Experiment 2 was the same as that of Experiment 1. There was no significant interaction between the distractor frequency and the phonological relatedness. These results demonstrate that word frequency has influence on the processes involved in selecting a correct lemma corresponding to an activated lexical concept in speech production.

• English Language & Literature Teaching
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• v.10 no.2
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• pp.67-87
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• 2004

The purpose of this paper is to show how the coda of a syllable and word-final clusters are represented in the English syllable structure. Previous theories on the syllable assume that there is only one segment in the coda position. And, as we know, the theories that license only one segment in the coda make it difficult to syllabicate the word-final cluster appropriately when more than two segments in the word-final cluster are encountered. I considered three approaches: the previous syllable structure (Selkirk, 1982; Borowsky 1989), sonority sequencing (Giegerich, 1992; Roca, 1999) and feature analysis (Goldsmith, 1990), But, all the considered methods don't give us a satisfactory explanation regarding word-final clusters. Finally, I will suggest a modified syllable representation as an alternative by placing two different appendixes under the Phonological Word which forms a constituent above the syllable node. From this it is possible to explain the former problematic word-final clusters including morphological information asan inflectional suffix in the structure.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.742-751
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• 2005

This paper proposes a novel text image watermarking algorithm based on word classification. The words are classified into K classes using simple features. Several adjacent words are grouped into a segment. and the segments are also classified using the word class information. The same amount of information is inserted into each of the segment classes. The signal is encoded by modifying some inter-word spaces statistics of segment classes. Subjective comparisons with conventional word-shift algorithms are presented under several criteria.

• English Language & Literature Teaching
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• v.17 no.1
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• pp.17-35
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• 2011

The purpose of this study was to explore the effect of an explicit word instruction for EFL beginning readers and their perception on the learning experience. Data were attained from 16 fourth graders who took English class as a development activity. Data include the results of pre- and post-test of high frequency sight word recognition, oral reading ability, students' survey responses, and teacher observation. The descriptive statistics were obtained for the result of the pre- and post-test. The findings from the student survey and teacher observation were also provided and interpreted to better understand the result of project and students' perception on the learning experience. The followings are the results of this study. The word recognition ability of the students was dramatically improved after the project. The students were satisfied with the overall learning experience perceiving it as helpful and fun learning. They expressed that the explicit word instruction helped their word recognition and reading ability. The results also supported that the confidence of students on their reading ability were heightened. Several suggestions are made for teachers and researchers on the word instruction for young EFL learners who are beginning readers.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• Speech Sciences
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• v.14 no.4
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• pp.115-128
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• 2007

The purposes of this paper were to develop an automatic script to collect such acoustic parameters as duration, intensity, pitch and the first two formant values of English words produced by two native Canadian speakers either alone or in a two-word phrase at a normal speed and to compare those values by the position in the phrases. A Praat script was proposed to obtain the comparable parameters at evenly divided time point of the target word. Results showed that the total duration of the word in the phrase was shorter than that of the word produced alone. That was attributed to the pronunciation style of the native speakers generally placing the primary word stress in the first word position. Also, the reduction ratio of the male speaker depended on the word position in the phrase while the female speaker didn't. Moreover, there were different contours of intensity and pitch by the position of the target word in the phrase while almost the same formant patterns were observed. Further studies would be desirable to examine those parameters of the words in the authentic speech materials.

• Phonetics and Speech Sciences
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• v.1 no.3
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• pp.109-115
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• 2009

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. In this paper, we investigate several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. At first, word distance method based on phone and syllable distances are considered. These methods use just Levenstein distance on phones or double Levenstein distance algorithm on syllables of candidates. Next, word similarity approaches are presented that they use characters' position information of word candidates. Each character's position is labeled to inserted, deleted, and correct position after alignment between source and target string. The word similarities are obtained from characters' positional probabilities which mean the frequency ratio of the same characters' observations on the position. From experimental results, we can find that the proposed methods are effective for removing non-similar words without loss of system performance from the N-best recognition candidates of the systems.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.55-62
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• 2009

This paper proposes an improved method to verify a keyword segment that results from a word spotting system. First a baseline word spotting system is implemented. In order to improve performance of the word spotting systems, we use a two-pass structure which consists of a word spotting system and an utterance verification system. Using the basic likelihood ratio test (LRT) based utterance verification system to verify the keywords, there have been certain problems which lead to performance degradation. So, we propose a method which uses phone-level log-likelihood ratios (PLLR) patterns in computing confidence measures for each keyword. The proposed method generates weights according to the PLLR patterns and assigns different weights to each phone in the process of generating confidence measures for the keywords. This proposed method has shown to be more appropriate to word spotting systems and we can achieve improvement in final word spotting accuracy.

• MALSORI
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• no.61
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• pp.31-48
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• 2007

This study investigated the roles of name agreement and word frequency in the English-Korean word translation task. Using the low-frequency homonyms with low name agreement as stimuli, Experiment 1 revealed that the name agreement of materials is a determinant which could modulate times to translate English words into Korean equivalents. On the contrary, Experiment 2 showed that the name agreement of materials does not play a decisive role in the translation task, using the low-frequency homonyms having high name agreement as stimuli. In Experiment 3, we identified that the frequency effects observed from previous two experiments are indeed brought about during the lexical access. Our findings suggest that the word frequencies of materials have a strong influence on English-Korean word translation times, and homonyms are represented independently each other in the lexeme level.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.16-23
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• 2001

In this paper, we propose the pseudo n-gram language models for speech recognition with middle size vocabulary compared to large vocabulary speech recognition using the statistical n-gram language models. The proposed method is that it is very simple method, which has the standard structure of ARPA and set the word probability arbitrary. The first, the 1-gram sets the word occurrence probability 1 (log likelihood is 0.0). The second, the 2-gram also sets the word occurrence probability 1, which can only connect the word start symbol and WORD, WORD and the word end symbol . Finally, the 3-gram also sets the ward occurrence probability 1, which can only connect the word start symbol , WORD and the word end symbol . To verify the effectiveness of the proposed method, the word recognition experiments are carried out. The preliminary experimental results (off-line) show that the word accuracy has average 97.7% for 452 words uttered by 3 male speakers. The on-line word recognition results show that the word accuracy has average 92.5% for 20 words uttered by 20 male speakers about stock name of 1,500 words. Through experiments, we have verified the effectiveness of the pseudo n-gram language modes for speech recognition.