Abstract: A method and a device for identifying an utterance from a signal is disclosed. the method includes acquiring a set of features for a respective segment of the signal, and an indication of an end-of-utterance moment in time in the signal corresponding to a moment in time after which the utterance has ended. The method includes determining an adjusted end-of-utterance moment in time, and labels for respective sets of features based on the adjusted end-of-utterance moment in time and time intervals of the corresponding segments. A given label is indicative of whether the utterance has ended during the corresponding segment associated with the respective set of features. The method also includes using the sets of features and the respective labels for training a Neural Network to predict during which segment of the digital audio signal the utterance has ended.

Abstract: A method and a system for generating textual representation of user spoken utterance is disclosed. The method comprises receiving an indication of the user spoken utterance; generating, at least two hypotheses; generating, by the electronic device, from the at least two hypotheses a set of paired hypotheses, a given one of the set of paired hypotheses including a first hypothesis paired with a second hypothesis; determining, for the given one of the set of paired hypotheses, a pair score; generating a set of utterance features, the set of utterance features being indicative of one or more characteristics associated with the user spoken utterance; ranking, the first hypothesis and the second hypothesis based at least on the pair score and the set of utterance features; and in response to the first hypothesis being a highest ranked hypothesis, selecting the first hypothesis as the textual representation of user spoken utterance.

Abstract: A method and a system for generating textual representation of user spoken utterance is disclosed. The method comprises receiving an indication of the user spoken utterance; generating, at least two hypotheses; generating, by the electronic device, from the at least two hypotheses a set of paired hypotheses, a given one of the set of paired hypotheses including a first hypothesis paired with a second hypothesis; determining, for the given one of the set of paired hypotheses, a pair score; generating a set of utterance features, the set of utterance features being indicative of one or more characteristics associated with the user spoken utterance; ranking, the first hypothesis and the second hypothesis based at least on the pair score and the set of utterance features; and in response to the first hypothesis being a highest ranked hypothesis, selecting the first hypothesis as the textual representation of user spoken utterance.

Abstract: A method and a device for identifying an utterance from a signal is disclosed. the method includes acquiring a set of features for a respective segment of the signal, and an indication of an end-of-utterance moment in time in the signal corresponding to a moment in time after which the utterance has ended. The method includes determining an adjusted end-of-utterance moment in time, and labels for respective sets of features based on the adjusted end-of-utterance moment in time and time intervals of the corresponding segments. A given label is indicative of whether the utterance has ended during the corresponding segment associated with the respective set of features. The method also includes using the sets of features and the respective labels for training a Neural Network to predict during which segment of the digital audio signal the utterance has ended.