Abstract
Pitch period evaluation of speech signal is used in many important applications of speech technology. However, among the existingmethods only some can work in case of non-linear and non-stationary signals. The main reason is that the pitch detection methods are basedon the assumption that speech production process is linear. Selection of pitch period estimation algorithm is always focuses on finding acompromise between time and frequency resolution, robustness, computational complexity and time delay. The aim of this paper is to develop a new method for estimating the pitch period based on empirical wavelet transformation. Method of constructing a family of adapted wavelets assumes that the filters depend on the information location in speech spectrum of the analyzed signal. Empirical wavelets are defined asbandpass filters for each segment of the speech signal. Instantaneous frequency characteristics are considered as pitch period detection features.Teager-Kaiser energy separation operator is used for its extraction. The comparison of this method with other pitch estimation algorithms ispresented.
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