Abstract: There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the 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 the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

Abstract: There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the 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 the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

Abstract: There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the 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 the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

Abstract: A method and server for determining an intent associated with a spoken user utterance are disclosed. The method includes determining at least one speech unit where each speech unit has textual data representative of a word or pause and has a corresponding segment of the digital audio signal. For each speech unit, the method includes generating a respective textual feature, generating a respective acoustic feature vector and generating a respective enhanced feature vector. The method also includes employing a neural network (NN) configured to determine the intent of the spoken user utterance by inputting into the NN the enhanced feature vectors. The NN has been trained to estimate a probability of the intent being of a given type.

Abstract: There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the 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 the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

Abstract: A method and server for determining an intent associated with a spoken user utterance are disclosed. The method includes determining at least one speech unit where each speech unit has textual data representative of a word or pause and has a corresponding segment of the digital audio signal. For each speech unit, the method includes generating a respective textual feature, generating a respective acoustic feature vector and generating a respective enhanced feature vector. The method also includes employing a neural network (NN) configured to determine the intent of the spoken user utterance by inputting into the NN the enhanced feature vectors. The NN has been trained to estimate a probability of the intent being of a given type.