• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.56-61
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• 2005

In this thesis, propose speech period and pitch detection for continuous speech recognition. This mathod is distinguishes between vowel and consonant to frame unit in continuous speech, for distinguishable voice. Powerful extraction of speech period could threshold energy make use of input signal to real noise environment. Also algorithm of this method distinguish between vowel and consonant at the same time in voice make use of zero crossing rate and short time energy to extractible speech period.

• Speech Sciences
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• v.5 no.1
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• pp.23-33
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• 1999

Mallat has shown that, with a proper choice of wavelet function, the local maxima of wavelet transformed signal indicate a sharp variation in the signal. Since the glottal closure causes sharp discontinuities in the speech signal, dyadic wavelet transform can be useful for detecting abrupt change in the voiced sounds, i.e., epochs. In this paper, we investigate the glottal closure instants obtained from the wavelet analysis of speech signal and compare them with those obtained from the EGG signal. Then, we detect pitch period of speech signal on the basis of these results. Experimental results demonstrated that local maxima of wavelet transformed signal give accurate estimation of epoch and pitch periods of voiced sound obtained by the proposed algorithm also correspond to those from EGG well.

• The Journal of the Acoustical Society of Korea
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• v.13 no.5
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• pp.77-82
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• 1994

In speech signal processing, it is very important to detect the pitch exactly. Although various methods for detecting the pitch of speech signals have been developed, it is difficult to exactly extract the pitch for wide range of speakers and various utterances. Thus we propose a new pitch detection algorithm which takes advantage of the G-peak extraction. It is a method to detect the pitch period of the voiced signals by finding MZCI (maximum zero-crossing interval) of the G-peak which is defined as cut-off bandwidth rate of LPF (low pass filter). This algorithm performs robustly with a gross error rate of 3.63% even in 0 dB SNR environement. The gross error rate for clean speech is only 0.18%. Also it is able to process all courses with high speed.

• Korean Journal of Culture and Social Issue
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• v.12 no.2
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• pp.101-114
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• 2006

This study was examined the effective nonverbal behavior cues of detecting suspects' lies in the investigation scene. In order to search the suspects who drank the alcohol liquor without a permission, 18 soldiers were interviewed. 8 solders had drunken alcohol and had lied when was asked(lie group). The other 10 soldiers hadn't drunken alcohol and had told the truth(truth group). The mean frequencies of nonverbal behaviors were compared lie group with truth group. The following behaviors were measured by frequency: vocal characteristics (high pitch of voice, speech hesitations, speech error, frequency of pauses, period of pauses, latency period), facial characteristics (gaze, smile, touching face, blinking, facial micro-expression), body movement (illustrators, hand and finger movement, leg and foot movement, head movement, trunk movement, shifting position). As results, this study found that deception cues were periods and frequencies of pause, micro-expression, head movements. The lie group had less periods and frequencies of pause, and more micro-expression, head movements than truth group. But, this study didn't found Othello's error cues.