The wavelet transform and its theory is one of the most exciting developments of the last decade. In fact, the wavelet transform has been developed independently for various different fields such as signal processing, image processing, audio and speech processing, communication, and mathematics. Due to the efficient time‐frequency localization and the multiresolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non‐stationary signals such as speech. In this paper, the wavelet transform and its theory will be first introduced, then comparisons between the wavelet transform and the classical short‐time Fourier transform approach to signal analysis will be provided. In addition, applying wavelet transforms in determining pitch, and segmenting consonant / vowel (C/V) parts as well as speech recognition will be discussed in this paper.