Abstract
In this paper, we have performed an evaluation of several time domain features for voiced/non-voiced classification of speech signal. We have chosen in a seamless way three features: autocorrelation function (ACF), average magnitude difference function (AMDF) and weighted ACF (WACF) to form three different classifiers. Experimental results were conducted on TIMIT database in clean and noisy environments. The white noise extracted from the NOISEX92 database has been incorporated to validate the developed classifiers. We have established an overall ranking of these classifiers based on the average value of the percentage of classification accuracy (Pc).
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