1. A computer-implemented method for speech recognition, comprising the steps of: registering, by means of an input device, an electrical signal representing speech and converting said signal to frequency or time-frequency domain to obtain a converted signal, analyzing said converted signal in an analysis module based on Dynamic Bayesian Network, configured to generate hypotheses of words (W) and their probabilities on the basis of observed acoustic (OA) signal features or acoustic (OA) and visual (OV) signal features, recognizing, on the basis of certain word (W) hypotheses and their probabilities, a text corresponding to said electrical signal representing speech, and further comprising: during the recognition process, inputting to said analysis module said observed acoustic (OA) signal features or acoustic (OA) and visual (OV) signal features which are determined for said converted signal in frequency or time-frequency domain in at least two parallel signal processing lines, wherein at least one of said signal processing lines is configured to receive said converted signal segmented in a different manner than the other signal processing lines such that at least some frames of at least one line of said signal processing lines can be shared between different states of said Dynamic Bayesian Network, and analyzing, in said analysis module, relations between said observed signal features for at least two distinct time segments.