• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.193-200
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• 2018

In a formal speech, the speaker's voice has a variety of effects on the listener. Depending on the voice characteristics, the effectiveness and efficiency of the speech change as well. In the presidential election, the candidate's voice characteristics will affect the cognition of voters. Thus, we need to understand about the a more effective voice of candidate. This study proves whether voters will change their cognition if the candidate changes owe voice. If the cognition of voters can be changed according to the changed voice characteristics of the candidate, we will be able to explain what voices are needed for the candidate. We will also be able to suggest the necessary voice change strategies for effective speech. We describe cognition change of the voters according to the change of the voices of the presidential candidates by the dimension of the sori-engineering and the cognitive dimension. Hence, this study explains the voice characteristics and change strategies needed for candidates for effective speech.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.24-44
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• 2014

This study explores the political meaning of candidate's speech and statement who run for the 18th presidential election in the viewpoint of communication. The visibility of the words and the connectivity between the words are analyzed in the viewpoint of structural aspect and the vision, policy. The visibility of the words is analyzed based on the frequency of the words mentioned in the speech or the statement. The connectivity between the words are analyzed based on the network analysis and expressed by graph. In the case of candidate Park, the key word is the happiness of the people and appointment. The key word for candidate Moon is regime change and the Korean Peninsula and the key word for candidate Ahn is the people and change. This study contributes positively to the study of candidate's discourse in the viewpoint of methodology by using network analysis and exploring scientifically the connectivity of the words. In the theoretical aspect this study uses the results of network analysis for revealing what is the leadership components in the speech and the statement. In conclusion, this study highlights the extension of the communication studies.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.791-797
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• 2008

The officer of the deck(OOD) may sometimes have to carry out lookout as well as handling of auto pilot without a quartermaster at sea. The purpose of this paper is to develop the ship's auto pilot control module using speech recognition in order to reduce the potential risk of one man bridge system. The feature parameters predicting the OOD's intention was extracted from the sample wheel orders written in SMCP(IMO Standard Marine Communication Phrases). We designed a pre-recognition procedure which could make some candidate words using DTW(Dynamic Time Warping) algorithm, a post-recognition procedure which made a final decision from the candidate words using the feature parameters. To evaluate the effectiveness of these procedures the experiment was conducted with 500 wheel orders.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.374-385
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• 2008

Speech act tagging is an important step in various dialogue applications, which recognizes speaker's intentions expressed in natural language utterances. Previous approaches such as rule-based and statistics-based methods utilize the speech acts of previous utterances and sentence features of the current utterance. This paper proposes a method that determines speech acts of the current utterance using the speech acts of the following utterances as well as previous ones. Using the features of following utterances yields the accuracy 95.27%, improving previous methods by 3.65%. Moreover, sentence features of the previous utterances are employed to maximally utilize the information available to the current utterance. By applying the proper probability model for each speech act, final accuracy of 97.97% is achieved.

• 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.2 no.4
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• pp.153-161
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• 2010

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. The previous study [1,2] investigated several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. This paper discusses the various improving techniques of removing non-similar recognition results. The mentioned methods include comparison penalties or weights, phone accuracy based on confusion information, weights candidates by ranking order and partial comparisons. Through experimental results, it is found that some proposed method keeps more accurate recognition results than the previous method's results.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.983-984
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• 2008

This paper proposes a variable break that can offset prediction error as well as a pre-selection methods, based on the variable break, for enhanced unit selection. In Japanese, a sentence consists of several APs (Accentual phrases) and MPs (Major phrases), and the breaks between these phrases must predicted to realize text-to-speech systems. An MP also consists of several APs and plays a decisive role in making synthetic speech natural and understandable because short pauses appear at its boundary. The variable break is defined as a break that is able to change easily from an AP to an MP boundary, or from an MP to an AP boundary. Using CART (Classification and Regression Trees), the variable break is modeled stochastically, and then we pre-select candidate units in the unit-selection process. As the experimental results show, it was possible to complement a break prediction error and improve the naturalness of synthetic speech.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.397-414
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• 2016

There will be coming soon a sequence of the so-called election season in Korea. That is the 20th session of general elections and the 19th presidential election which are taking place one after another. When we talk about presidential candidate, we may realize that the choice were relied upon candidate's facial appearance and speech, voice, dress, and so on. One poll showed that capacity and policy were ranked high and figure and image were ranked next by asking "what is the most important factor when you vote for the president?" This study looked into the candidate's face of a newly elected public office through the election voting. I studied how the factors of sex and beauty may affect on the election results. I analyse survey of candidate's photo on the election leaflets. Analyzed result said that candidate's competency and trust have strong relations in the voting choice rather than beauty even if it is statistically significant. I also find that both beauty and competency are positively interrelated.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.178-178
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.

• MALSORI
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• no.66
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• pp.73-86
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• 2008

Beamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamformning, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Bark's scale spectral distortion (BSD).