• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.358-371
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• 2003

Automatic word spacing is a process of deciding correct boundaries between words in a sentence including spacing errors. It is very important to increase the readability and to communicate the accurate meaning of text to the reader. The previous statistical approaches for automatic word spacing do not consider the previous spacing state, and thus can not help estimating inaccurate probabilities. In this paper, we propose two statistical word spacing models which can solve the problem of the previous statistical approaches. The proposed models are based on the observation that the automatic word spacing is regarded as a classification problem such as the POS tagging. The models can consider broader context and estimate more accurate probabilities by generalizing hidden Markov models. We have experimented the proposed models under a wide range of experimental conditions in order to compare them with the current state of the art, and also provided detailed error analysis of our models. The experimental results show that the proposed models have a syllable-unit accuracy of 98.33% and Eojeol-unit precision of 93.06% by the evaluation method considering compound nouns.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.259-266
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• 2018

In this paper, we propose a method to classify a document using a Recurrent Neural Network by extracting features considering word sense and contexts. Word2vec method is adopted to include the order and meaning of the words expressing the word in the document as a vector. Doc2vec is applied for considering the context to extract the feature of the document. RNN classifier, which includes the output of the previous node as the input of the next node, is used as the document classification method. RNN classifier presents good performance for document classification because it is suitable for sequence data among neural network classifiers. We applied GRU (Gated Recurrent Unit) model which solves the vanishing gradient problem of RNN. It also reduces computation speed. We used one Hangul document set and two English document sets for the experiments and GRU based document classifier improves performance by about 3.5% compared to CNN based document classifier.

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.435-442
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• 2008

The n-gram model is appropriate for languages, such as English, in which the word-order is grammatically rigid. However, it is not suitable for Korean in which the word-order is relatively free. Previous work proposed a twoply HMM that reflected the characteristics of Korean but failed to reflect word-order structures among words. In this paper, we define a new segment unit which combines two words in order to reflect the characteristic of word-order among adjacent words that appear in verbal morphemes. Moreover, we propose a two-path language model that estimates probabilities depending on the context based on the proposed segment unit. Experimental results show that the proposed two-path language model yields 25.68% perplexity improvement compared to the previous Korean language models and reduces 94.03% perplexity for the prediction of verbal morphemes where words are combined.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.328-338
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• 2008

Most speech recognition systems that have a large capacity and high recognition rates are isolated word speech recognition systems. In order to extend the scope of recognition, it is necessary to increase the number of words that are to be searched. However, it shows a problem that exhibits a decrease in the system performance according to the increase in the number of words. This paper defines the context information that affects speech recognition in a ubiquitous environment to solve such a problem and develops user localization method using inertial sensor and RFID. Also, we develop a new speech recognition system that demonstrates better performances than the existing system by establishing a word model domain of a speech recognition system by context information. This system shows operation without decrease of recognition rate in smart home environment.

• Journal of Korean Academy of Nursing
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• v.10 no.2
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• pp.21-40
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• 1980

In order to take cue of the dying persons and their survivors in a more positive and affirmative atti-tube. and to understand the valuable meaning of and dying. a survey was performed to 550 cases of health care personnels including 116 nursing students. 238 medical students. 137 nurses. and 59 doctors. Samplings were made through census Procedure from the entire group of medical and nursing students in College of Medicine. Chung-Ang University. and of licenced nurses and doctors in Chung-Ang University Hospital. and in Han-Gang Sacred Heart Hospital from the first to the end of march. 1980. These collected data were computerized at KIST by SPSS programming and were statistically analyzed by chi-square test. Through content analysis of the word associated with death and descriptive analysis of the death-re-lated variables. the following conclusion in is reached. First. Total numbers of death-word percieved by health care personnels were 198 kinds. Among them, 40 kinds of words associated with death were responded from than 1% of the total. As to the 10 death related word responded by free word association method. it was revealed that individual average number of death related word was 7.70 word. which came from higher number of words in the senior students (8.96 word) or the graduates (8.10 word) compared with the freshman (6.84 word). Second. In Content specific analysis of the death related word. more frequently perceived types summarized as the following order; the affective context of death. the diseases. the disasters. the religion, the funeral ceremonies. the separation, the drakness. and the life. Third. The most prevalent 10 words associated with death which the the respondents gave response to the the first recalling word. were as following o order； the dieases. the sadness, the vanity. the darkness, the frustration. the suicide. the incurable dieases, the graves. the dead. and the catastrophes. By sex, the diease is outstanding in females, but the vanity is in males. By occupation. the vanity and the dead was frequently observed in student group including senior students. while the incurable dieases presented by doctors. Fourth. In health care personnels. the first perceived ages of death were 11.47 $\pm$3.33 years (8.14- 15.80 years). Among them. senior students were inclined to percept death at the earliest age of life (11.28years). while doctors and nurses perceived death later in their life (12.98 years). Fifth, It is revealed in this survey that the most frequently responded death perceiving motives by health care personnels ar“psychological conflict”and“death of those around them”. Death perceiving motives can be classified in two factors; personality and life circumstances. Sixth It is of interest that only 11.3% health care personnels was found to feel death as inevitable or acceptable event. whereas 58.3% deny or reject it.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.77-89
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• 2018

Speech articulators are coordinated for the purpose of segmental constriction in terms of a task. In particular, vertical jaw movements repeatedly contribute to consonantal as well as vocalic constriction. The current study explores vertical jaw movements in conjunction with bilabial constriction in bilabial stop /p/ in the context /a/-to-/a/. Revisiting kinematic data of /p/ collected using the electromagenetic midsagittal articulometer (EMMA) method from seven (four female and three male) speakers of Seoul Korean, we examined maximum vertical jaw position, its relative timing with respect to the upper and lower lips, and lip aperture minima. The results of those dependent variables are recapitulated in terms of linguistic (different word boundaries) and paralinguistic (different speech rates) factors as follows. Firstly, maximum jaw height was lower in the across-word boundary condition (across-word < within-word), but it did not differ as a function of different speech rates (comfortable = fast). Secondly, more reduction in the lip aperture (LA) gesture occurred in fast rate, while word-boundary effects were absent. Thirdly, jaw raising was still in progress after the lips' positional extrema were achieved in the within-word condition, while the former was completed before the latter in the across-word condition. Lastly, relative temporal lags between the jaw and the lips (UL and LL) were more synchronous in fast rate, compared to comfortable rate. When these results are considered together, it is possible to posit that speakers are not tolerant of lenition to the extent that it is potentially realized as a labial approximant in either word-boundary condition while jaw height still manifested lower jaw position in the across-word boundary condition. Early termination of vertical jaw maxima before vertical lower lip maxima across-word condition may be partly responsible for the spatial reduction of jaw raising movements. This may come about as a consequence of an excessive number of factors (e.g., upper lip height (UH), lower lip height (LH), jaw angle (JA)) for the representation of a vector with two degrees of freedom (x, y) engaged in a gesture-based task (e.g., lip aperture (LA)). In the task-dynamic application toolkit, the jaw angle parameter can be assigned numerical values for greater weight in the across-word boundary condition, which in turn gives rise to lower jaw position. Speech rate-dependent spatial reduction in lip aperture may be able to be resolved by means of manipulating activation time of an active tract variable in the gestural score level.

• MALSORI
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• v.68
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• pp.33-47
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• 2008

In this paper, we propose a hybrid acoustic and pronunciation model adaptation method based on context dependency for Korean-English speech recognition. The proposed method is performed as follows. First, in order to derive pronunciation variant rules, an n-best phoneme sequence is obtained by phone recognition. Second, we decompose each rule into a context independent (CI) or a context dependent (CD) one. To this end, it is assumed that a different phoneme structure between Korean and English makes CI pronunciation variabilities while coarticulation effects are related to CD pronunciation variabilities. Finally, we perform an acoustic model adaptation and a pronunciation model adaptation for CI and CD pronunciation variabilities, respectively. It is shown from the Korean-English speech recognition experiments that the average word error rate (WER) is decreased by 36.0% when compared to the baseline that does not include any adaptation. In addition, the proposed method has a lower average WER than either the acoustic model adaptation or the pronunciation model adaptation.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.175-181
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• 2003

Because a word of Korean digits is a syllable and deeply coarticulatied in connected digits, some recognition models based on demisyllables have been proposed by researchers. However, they could not show an excellent recognition results yet. This paper proposes a recognition model based on extended and context-dependent demisyllables, such as a tri-demisyllable like a tri-phone, for the Korean 4-connected digits recognition. For experiments, we use a toolkit of HTK 3.0 for building this model of continuous HMMs using training Korean connected digits from SiTEC database and for recognizing unknown ones. The results show that the recognition rate is 92% and this model has an ability to improve the recognition performance of Korean connected digits.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.49-59
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• 2020

As technologies have been developing, our lives are getting easier. Today we are surrounded by the new technologies such as AI and IoT. Moreover, the word, "smart" is a very broad one because we are trying to change our daily environment into smart one by using those technologies. For example, the traditional workplaces have changed into smart offices. Since the 3rd industrial revolution, we have used the touch interface to operate the machines. In the 4th industrial revolution, however, we are trying adding the speech recognition module to the machines to operate them by giving voice commands. Today many of the things are communicated with human by voice commands. Many of them are called AI things and they do tasks which users request and do tasks more than what users request. In the 4th industrial revolution, we use smartphones all the time every day from the morning to the night. For this reason, the privacy using phone is not guaranteed sometimes. For example, the caller's voice can be heard through the phone speaker when accepting a call. So, it is needed to protect privacy on smartphone and it should work automatically according to the user context. In this aspect, this paper proposes a method to adjust the voice volume for call to protect privacy on smartphone according to the user context.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.21-27
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• 1999

In this paper, we propose an improved MLP-based English pronunciation dictionary generator to apply to the variable vocabulary word recognizer. The variable vocabulary word recognizer can process any words specified in Korean word lexicon dynamically determined according to the current recognition task. To extend the ability of the system to task for English words, it is necessary to build a pronunciation dictionary generator to be able to process words not included in a predefined lexicon, such as proper nouns. In order to build the English pronunciation dictionary generator, we use context-dependent grapheme-to-phoneme multi-layer perceptron(MLP) architecture for each grapheme. To train each MLP, it is necessary to obtain grapheme-to-phoneme training data from general pronunciation dictionary. To automate the process, we use dynamic programming(DP) algorithm with some distance metrics. For training and testing the grapheme-to-phoneme MLPs, we use general English pronunciation dictionary with about 110 thousand words. With 26 MLPs each having 30 to 50 hidden nodes and the exception grapheme lexicon, we obtained the word accuracy of 72.8% for the 110 thousand words superior to rule-based method showing the word accuracy of 24.0%.