Abstract: A signal processing system is disclosed which is implemented using Gaussian Mixture Model (GMM) based Hidden Markov Model (HMM), or a GMM alone, parameters of which are constrained during its optimization procedure. Also disclosed is a constraint system applied to input vectors representing the input signal to the system. The invention is particularly, but not exclusively, related to speech recognition systems. The invention reduces the tendency, common in prior art systems, to get caught in local minima associated with highly anisotropic Gaussian components—which reduces the recognizer performance—by employing the constraint system as above whereby the anisotropy of such components are minimized. The invention also covers a method of processing a signal, and a speech recognizer trained according to the method.

Abstract: A method of simultaneously reading out squared components of a vector represented by a plurality of holographic gratings comprising the steps of generating a reference wave of known amplitude and diffracting the reference wave from each holographic grating to produce a plurality of diffracted waves each of which has an intensity proportional to the square of a component of the vector. The method allows squared components of the vector to be read out in parallel increasing the speed at which they may be processed compared to standard electronic techniques.