Abstract: A method includes obtaining an audio input of a person speaking, where the audio input is captured by an electronic device. The method also includes, for each of multiple language types, (i) determining a first probability that the person is speaking in the language type by applying a trained spoken language identification model to the audio input, (ii) determining at least one second probability that the person is speaking in the language type based on at least one characteristic of the person or the electronic device, and (iii) determining a score for the language type based on a weighted sum of the first and second probabilities. The method further includes identifying the language type associated with a highest score as a spoken language of the person in the audio input.

Abstract: A method, an electronic device, and non-transitory machine-readable medium are provided. The method includes receiving, on an electronic device, a request to perform an action. The method also includes deriving an aggregated predicted confidence level using one or more confidence levels. The one or more confidence levels are based on usage information and context of the electronic device. The method further includes determining an execution engine to process the request based on the aggregated predicted confidence level. The method additionally includes providing at least a portion of the request to the execution engine for processing.

Abstract: An apparatus for processing speech data may include a processor configured to: separate speech signals from an input speech; identify a language of each of the speech signals that are separated from the input speech; extract speaker embeddings from the speech signals based on the language of each of the speech signals, using at least one neural network configured to receive the speech signals and output the speaker embeddings; and identify a speaker of each of the speech signals by iteratively clustering the speaker embeddings.

Abstract: A method includes training a set of teacher models. Training the set of teacher models includes, for each individual teacher model of the set of teacher models, training the individual teacher model to transcribe unlabeled audio samples and predict a pseudo labeled dataset having multiple labels. At least some of the unlabeled audio samples contain named entity (NE) audio data. At least some of the labels include transcribed NE labels corresponding to the NE audio data. The method also includes correcting at least some of the transcribed NE labels using user-specific NE textual data. The method further includes retraining the set of teacher models based on the pseudo labeled dataset from a selected one of the teacher models, where the selected one of the teacher models predicts the pseudo labeled dataset more accurately than other teacher models of the set of teacher models.

Abstract: A method, an electronic device, and non-transitory machine-readable medium are provided. The method includes receiving, on an electronic device, a request to perform an action. The method also includes deriving an aggregated predicted confidence level using one or more confidence levels. The one or more confidence levels are based on usage information and context of the electronic device. The method further includes determining an execution engine to process the request based on the aggregated predicted confidence level. The method additionally includes providing at least a portion of the request to the execution engine for processing.