Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals

This 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 autocorrelation 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. Autocorrelation function is used here to show that the voiced segment of speech remains periodic after applying autocorrelation function, while unvoiced signals lose their periodicity. Experimental results have been presented in this paper to verify theoretical studies.