Madiha JalilFaran Awais ButtAhmed Malik2013-11-072013-11-072013Jalil, M., F. A. Butt, et al. (2013). Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals. Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, IEEE.https://escholar.umt.edu.pk/handle/123456789/845This paper presents different methods of separating voiced and unvoiced segments of a speech signals. These methods are based on short time energy calculation, short time magnitude calculation, and zero crossing rate calculation and on the basis of auto-correlation of different segments of speech signals. From theoretical studies, it has been observed that energy and magnitude for voiced segments is high, whereas ZCR rate is low for voiced signals. Auto-correlation function is used here to show that the voiced segment of speech remains periodic after applying auto-correlation function, while unvoiced signals lose their periodicity. Experimental results have been presented in this paper to verify theoretical studies.enZero Crossing RateShort Time EnergyAutocorrelationVoicedUnvoicedArticle