Entropy and Speech

In this thesis, we study the representation of speech signals and the estimation of information-theoretical measures from observations containing features of the speech signal. The main body of the thesis consists of four research papers. Paper A presents a compact representation of the speech signal that facilitates perfect reconstruction. The representation is constituted of models, model parameters, and signal coefficients. A difference compared to existing speech representations is that we seek a compact representation by adapting the models to maximally concentrate the energy of the signal coefficients according to a selected energy concentration criterion. The individual parts of the representation are closely related to speech signal properties such as spectral envelope, pitch, and voiced/unvoiced signal coefficients, beneficial for both speech coding and modification. From the information-theoretical measure of entropy, performance limits in coding and classification can be derived. Papers B and C discuss the estimation of differential entropy. Paper B describes a method for estimation of the differential entropies in the case when the set of vector observations (from the representation) lie on a lower-dimensional surface (manifold) in the embedding space. In contrast to the method presented in Paper B, Paper C introduces a method where the manifold structures are destroyed by constraining the resolution of the observation space. This facilitates the estimation of bounds on classification error rates even when the manifolds are of varying dimensionality within the embedding space. Finally, Paper D investigates the amount of shared information between spectral features of narrow-band (0.3-3.4 kHz) and high-band (3.4-8 kHz) speech. The results in Paper D indicate that the information shared between the high-band and the narrow-band is insufficient for high-quality wideband speech coding (0.3-8 kHz) without transmission of extra information describing the high-band.