• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.89-95
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• 2016

The purpose of this work is to analyze and synthesize the pseudo-periodicity of voice using a source model. A speech signal has periodic characteristics; however, it is not completely periodic. While periodicity contributes significantly to the production of prosody, emotional status, etc., pseudo-periodicity contributes to the distinctions between normal and abnormal status, the naturalness of normal speech, etc. Measurement of pseudo-periodicity is typically performed through parameters such as jitter and shimmer. For studying the pseudo-periodic nature of voice in a controlled environment, through collected natural voice, we can only observe the distributions of the parameters, which are limited by the size of collected data. If we can generate voice samples in a controlled manner, experiments that are more diverse can be conducted. In this study, the probability distributions of vowel pitch variation are obtained from the speech signal. Based on the probability distribution of vocal folds, pulses with a designated jitter value are synthesized. Then, the target and re-analyzed jitter values are compared to check the validity of the method. It was found that the jitter synthesis method is useful for normal voice synthesis.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.237-246
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• 1998

We put the pseudo-random number generator into catagories like MiCG, MuCG, URG, ICG, EICG, and test uniformity and independency by 10,000 times through n empirical trial after selecting this random number generator. Here, from a fraction of data(20, 40, 60, 80, 100) with a significance level of 0.1, 0.05 and 0.01, we drive cumulative frequency with K-S, $X^{2}$, poker, run, autocorrelation test. As a result from the uniformity and independency among five random number generators based on all these data, all random number generator except EICG passed uniformity and independency test, and the URG turn out to be excellent in periodicity.