Abstract: A method and a system for training a machine-learning algorithm (MLA) to determine a user class of a user of an electronic device are provided. The method comprises: receiving a training audio signal representative of a training user utterance; soliciting, by the processor, a plurality of assessor-generated labels for the training audio signal, the given one of the plurality of assessor-generated labels being indicative of whether the training user is perceived to be one of a first class and a second class; generating an amalgamated assessor-generated label for the training audio signal, the amalgamated assessor-generated label being indicative of a label distribution of the plurality of assessor-generated labels between the first class and the second class; generating a training set of data including the training audio signal and the amalgamated assessor-generated to train the MLA to determine the user class of the user producing an in-use user utterance.

Abstract: There is provided servers and methods of generating a waveform based on a spectrogram and a noise input. The method includes acquiring a trained flow-based vocoder including invertible blocks, and an untrained feed-forward vocoder including non-invertible blocks, which form a student-teacher network. The method includes executing a training process in the student-teacher network during which the server generates (i) a teacher waveform by the trained flow-based vocoder using a first spectrogram and a first noise input, (ii) a student waveform by the untrained feed-forward vocoder using the first spectrogram and the first noise input, and (iii) a loss value for the given training iteration using the teacher waveform and the student waveform. The server then trains the untrained feed-forward vocoder to generate the waveform. The trained feed-forward vocoder in then used lieu of the trained flow-based vocoder for generating waveforms based on spectrograms and noise inputs.

Abstract: A method and a system for training a machine-learning algorithm (MLA) to determine a user class of a user of an electronic device are provided. The method comprises: receiving a training audio signal representative of a training user utterance; soliciting, by the processor, a plurality of assessor-generated labels for the training audio signal, the given one of the plurality of assessor-generated labels being indicative of whether the training user is perceived to be one of a first class and a second class; generating an amalgamated assessor-generated label for the training audio signal, the amalgamated assessor-generated label being indicative of a label distribution of the plurality of assessor-generated labels between the first class and the second class; generating a training set of data including the training audio signal and the amalgamated assessor-generated to train the MLA to determine the user class of the user producing an in-use user utterance.