Abstract: An automatic speech recognition system includes a speech recognition model and a training module. The training module has a training interface to receive from a user training data. The training data includes new vocabulary to be stored in a vocabulary register and at least one of: vocabulary phonetic notation to be stored in a phonetics register, vocabulary use examples to be stored in an examples register and vocabulary speech recordings to be stored in a recordings register, and a speech recognition model interface to perform a training procedure to train the speech recognition model based on the training data stored in the registers.

Abstract: A computer-implemented method for speech recognition, comprising the steps of: registering (201) by means of an input device (102A), electrical signal representing speech and converting the signal to frequency or time-frequency domain (202), analyzing the signal in an analysis module based on Dynamic Bayesian Network (205) configured to generate hypotheses of words (W) and their probabilities on the basis of observed signal features (OA, OV), recognizing (209) a text corresponding to the electrical signal representing speech on the basis of certain word (W) hypotheses and their probabilities.

Abstract: A computer-implemented method for speech recognition, comprising the steps of: registering (201) by means of an input device (102A), electrical signal representing speech and converting the signal to frequency or time-frequency domain (202), analyzing the signal in an analysis module based on Dynamic Bayesian Network (205) configured to generate hypotheses of words (W) and their probabilities on the basis of observed signal features (OA, OV), recognizing (209) a text corresponding to the electrical signal representing speech on the basis of certain word (W) hypotheses and their probabilities.