• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.131-138
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• 2014

A close-copy stylization of intonation curve was used for an acoustical analysis of emotional speech. For the analysis, 408 utterances of five emotions (happiness, anger, fear, neutral and sadness) were processed to extract acoustical feature values. The results show that certain pitch point features (pitch point movement time and pitch point distance within a sentence) and sentence level features (pitch range of a final pitch point, pitch range of a sentence and pitch slope of a sentence) are affected by emotions. Pitch point movement time, pitch point distance within a sentence and pitch slope of a sentence show no significant difference between male and female participants. The emotions with high arousal (happiness and anger) are consistently distinguished from the emotion with low arousal (sadness) in terms of these acoustical features. Emotions with higher arousal show steeper pitch slope of a sentence. They have steeper pitch slope at the end of a sentence. They also show wider pitch range of a sentence. The acoustical analysis in this study implies the possibility that the measurement of these acoustical features can be used to cluster and identify emotions of speech.

• Phonetics and Speech Sciences
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• v.7 no.1
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• pp.97-105
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• 2015

This research mainly focuses on intonational characteristics of synchronous English speech. Intonation contours were extracted from 1,848 utterances produced in two different speaking modes (solo vs. synchronous) by 28 (12 women and 16 men) native speakers of English. Synchronous speech is found to be slower than solo speech. Women are found to speak slower than men. The effect size of speech rate caused by different speaking modes is greater than gender differences. However, there is no interaction between the two factors (speaking modes vs. gender differences) in terms of speech rate. Analysis of pitch point features has it that synchronous speech has smaller Pt (pitch point movement time), Pr (pitch point pitch range), Ps (pitch point slope) and Pd (pitch point distance) than solo speech. There is no interaction between the two factors (speaking modes vs. gender differences) in terms of pitch point features. Analysis of sentence level features reveals that synchronous speech has smaller Sr (sentence level pitch range), Ss (sentence slope), MaxNr (normalized maximum pitch) and MinNr (normalized minimum pitch) but greater Min (minimum pitch) and Sd (sentence duration) than solo speech. It is also shown that the higher the Mid (median pitch), the MaxNr and the MinNr in solo speaking mode, the more they are reduced in synchronous speaking mode. Max, Min and Mid show greater speaker discriminability than other features.

• Phonetics and Speech Sciences
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• v.6 no.4
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• pp.79-90
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• 2014

An intonation curve stylization was used for an acoustical analysis of English speech. For the analysis, acoustical feature values were extracted from 1,848 utterances produced with normal and fast speech rate by 28 (12 women and 16 men) native speakers of English. Men are found to speak faster than women at normal speech rate but no difference is found between genders at fast speech rate. Analysis of pitch point features has it that fast speech has greater Pt (pitch point movement time), Pr (pitch point pitch range), and Pd (pitch point distance) but smaller Ps (pitch point slope) than normal speech. Men show greater Pt, Pr, and Pd than women. Analysis of sentence level features reveals that fast speech has smaller Sr (sentence level pitch range), Sd (sentence duration), and Max (maximum pitch) but greater Ss (sentence slope) than normal speech. Women show greater Sr, Ss, Sp (pitch difference between the first pitch point and the last), Sd, MaxNr (normalized Max), and MinNr (normalized Min) than men. As speech rate increases, women speak with greater Ss and Sr than men.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.5
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• pp.1126-1133
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• 1994

When designing an internal gear. the bending strength around pitch point as well as that at tooth root fillet should be considered because the bending stress around pitch point may occur as high as that at tooth root fillet. In this study, including stress state around pitch point, the bending strength (tensile side and compressive side) of internal gear tooth is investigated by the use of the finite element method(FEM) with regarding many influencing factors of cutter and gear geometries. Then, the critical sections around pitch point and at tooth root fillet are determined, and the simple formulae based on nominal stresses(bending, compressive, and shear) are derived for the calculations of actual stresses as the functions of tooth thicknesses and radii of curvatures of involute and fillet curve at those critical sections. The stresses calculated by the formulae agree well with those by the FEM. And the bending stresses around pitch point and at tooth root are easily estimated by the use of those formulae, therefore, those formulae are useful for the purpose of the design or the bending strength estimation of internal gear.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.23-28
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• 2006

Pitch period that is a important factor in speech signal processing is used in various applications such as speech recognition, speaker identification, speech analysis and synthesis. So many pitch detection algorithms have been studied until now. AMDF which is one of pitch period detection algorithms chooses the time interval from valley point to valley point as pitch period. In selection of valley point to detect pitch period, complexity of the algorithm is increased. So in this paper we proposed the simple algorithm using rotation transform of AMDF that detects global minimum valley point as pitch period of speech signal and compared it with existing methods through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.515-519
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• 2005

Pitch period that is a important factor in speech signal processing is used in various applications such as speech recognition, speaker identification, speech analysis and synthesis. So many pitch detection algoritms have been studied until now. AMDF which is one of pitch period detection algorithms chooses the time interval from valley point to valley point as pitch period. In selection of valley point to detect pitch period, complexity of the algoritm is increased. So in this paper we proposed the simple algorithm using modified AMDF that detects global minimum valley point as pitch period of speech signal and compared existing methods with it through simulation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.1
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• pp.75-86
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• 2023

The angle of attack is highly sensitive to pitch point in the airfoil shape and the decline of pitch point value induces smaller angle of attack, which implies that airfoil profile possessing closer pitch point to the airfoil tip reacts more sensitively to upcoming wind. The method of conformal transformation functions is employed for airfoil profiles and airfoil surfaces are expressed with a trigonometric series form. Attack angle and ideal lift coefficient distributions are investigated for various airfoil profiles in wind turbine blade regarding conformal transformation and pitch point. The conformed angle function representing the surface angle of airfoil shape generates various attack angle distributions depending on the choice of surface angle function. Moreover, ideal attack angle and ideal lift coefficient are susceptible to the choice of airfoil profiles and uniform loading area. High ideal attack angle signifies high pliability to upcoming wind, and high ideal lift coefficient involves high possibility to generate larger electric energy. According to results obtained pitch point, airfoil shape, uniform loading area, and the conformed airfoil surface angle function are crucial factors in the determination of angle of attack.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.259-262
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• 1993

In this paper, a new pitch beginning point detection method by extracting the G-peak, is proposed. By the speech production model, the area of the first peak on a pitch interval of speech signals is emphasized. By using the above characteristics, this method have more advantages than the others for pitch beginning point detection. The defective decision caused by an impulsive noise is minimized and the pre-filtering is not necessary for this method, because the integration of signals takes place in the process.

• Carbon letters
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• v.15 no.2
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• pp.129-135
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• 2014

Spinnable pitch for melt-electrospinning was obtained from pyrolized fuel oil by electron beam (E-beam) radiation treatment. The modified pitch was characterized by measuring its elemental composition, softening point, viscosity, molecular weight, and spinnability. The softening point and viscosity properties of the modified pitch were influenced by reforming types (heat or E-beam radiation treatment) and the use of a catalyst. The softening point and molecular weight were increased in proportion to absorbed doses of E-beam radiation and added $AlCl_3$ due to the formation of pitch by free radical polymerization. The range of the molecular weight distribution of the modified pitch becomes narrow with better spinning owing to the generated aromatic compounds with similar molecular weight. The diameter of melt-electrospun pitch fibers under applied power of 20 kV decreased 53% ($4.7{\pm}0.9{\mu}m$) compared to that of melt-spun pitch fibers ($10.2{\pm}2.8{\mu}m$). It is found that E-beam treatment for reforming could be a promising method in terms of time-savings and cost-effectiveness, and the melt-electrospinning method is suitable for the preparation of thinner fibers than those obtained with the conventional melt-spinning method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.481-484
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• 2006

Pitch period detection algorithms are applied to various speech signal processing fields such as speech recognition, speaker identification, speech analysis and synthesis. Furthermore, many pitch detection algorithms of time and frequency domain have been studied until now. AMDF(average magnitude difference function) ,which is one of pitch period detection algorithms, chooses a time interval from the valley point to the valley point as the pitch period. AMDF has a fast computation capacity, but in selection of valley point to detect pitch period, complexity of the algorithm is increased. In order to apply pitch period detection algorithms to the real world, they have robust prosperities against generated noise in the subway environment etc. In this paper we proposed the modified AMDF algorithm which detects the global minimum valley point as the pitch period of speech signals and used speech signals of noisy environments as test signals.