In this paper we present orthogonal overlapped block transforms as a frame synchronized signal analysis tool with the capability of arbitrary multiresolution time-spectral decomposition of speech signals. Our prime interest is in the representation of nonstationary discrete-time signals in terms of wavelet packets, and we concentrate on their fast transform algorithms. Wavelet packet representations provide a local time-spectral description which reveals the nonstationary nature of a signal. They allow the speech signal to be accurately parameterised for such applications as speech and speaker recognition, where a front-end is responsible for the frame synchronized feature extraction. In this case the fast overlapped block transform algorithms represent an elegant and efficient solution to the implementation of wavelet packet transforms, since their FFT-like lattice block structure provides all possible multiresolution time-spectral coefficients. The frame synchronization is also preserved in subbands which allows a new subband-based approach for speech recognition.