A method of determining a speaker, the speaker selectable from a set of registered users associated with a voice-controllable device, the method executable by an electronic device configured to execute a Machine Learning Algorithm (MLA), the method comprising: receiving, by the electronic device , an indication of a user utterance, the user utterance having been produced by the speaker; executing, by the electronic device the MLA, the MLA having been trained to analyze voice features of the user utterance to generate, for each given one of the set of registered users , a first probability parameter indicative of the speaker of the user utterance being the given one of the set of registered users ; executing, by the electronic device , a user frequency analysis of the use of the voice-controllable device by each given one of the set of registered users to generate, for each given one of the set of registered users , a second probability parameter, the second probability parameter being an apriori frequency based probability; generating, for the electronic device , for each given one of the set of registered users an amalgamated probability based on the first probability and the second probability associated therewith; selecting, by the electronic device , the given one of the set of registered users as the speaker of the user utterance, the given one being associated with a highest value of the amalgamated probability value, characterised in that the user frequency analysis weighs a sub-set of apriori probability for each one of the set of registered users , the sub-set including a pre-determined number of more recent past calculations, and optionally the set of registered users comprises a registered user and a guest user, and the method further comprises setting a pre-determined minimum value of the apriori probability under which the apriori probability for the guest user can not drop. receiving, by the electronic device , an indication of a user utterance, the user utterance having been produced by the speaker; executing, by the electronic device the MLA, the MLA having been trained to analyze voice features of the user utterance to generate, for each given one of the set of registered users , a first probability parameter indicative of the speaker of the user utterance being the given one of the set of registered users ; executing, by the electronic device , a user frequency analysis of the use of the voice-controllable device by each given one of the set of registered users to generate, for each given one of the set of registered users , a second probability parameter, the second probability parameter being an apriori frequency based probability; generating, for the electronic device , for each given one of the set of registered users an amalgamated probability based on the first probability and the second probability associated therewith; selecting, by the electronic device , the given one of the set of registered users as the speaker of the user utterance, the given one being associated with a highest value of the amalgamated probability value, characterised in that the user frequency analysis weighs a sub-set of apriori probability for each one of the set of registered users , the sub-set including a pre-determined number of more recent past calculations, and optionally the set of registered users comprises a registered user and a guest user, and the method further comprises setting a pre-determined minimum value of the apriori probability under which the apriori probability for the guest user can not drop. <EOS>