Abstract: A method includes receiving audio data corresponding to an utterance spoken by a user and processing, using a first recognition model, the audio data to generate a non-contextual candidate hypothesis as output from the first recognition model. The non-contextual candidate hypothesis has a corresponding likelihood score assigned by the first recognition model. The method also includes generating, using a second recognition model configured to receive personal context information, a contextual candidate hypothesis that includes a personal named entity. The method also includes scoring, based on the personal context information and the corresponding likelihood score assigned to the non-contextual candidate hypothesis, the contextual candidate hypothesis relative to the non-contextual candidate hypotheses.

Abstract: A method includes receiving, from a speech recognizer, multiple existing candidate hypotheses for an utterance. Each existing candidate hypothesis has a corresponding likelihood score assigned by the speech recognizer. The method also includes generating, using a correction module configured to receive the multiple candidate hypotheses as input, a new candidate hypothesis and determining, using a comparison model configured to receive the corresponding likelihood score assigned to one of the multiple existing candidate hypotheses as input, a corresponding likelihood score. The method also includes ranking the multiple existing candidate hypotheses and the new candidate hypothesis based on the corresponding likelihood scores assigned to the multiple existing candidate hypothesis by the speech recognizer and the corresponding likelihood score for the new candidate hypothesis.

Abstract: Aspects of this disclosure are directed to accurately transforming speech data into one or more word strings that represent the speech data. A speech recognition device may receive the speech data from a user device and an indication of the user device. The speech recognition device may execute a speech recognition algorithm using one or more user and acoustic condition specific transforms that are specific to the user device and an acoustic condition of the speech data. The execution of the speech recognition algorithm may transform the speech data into one or more word strings that represent the speech data. The speech recognition device may estimate which one of the one or more word strings more accurately represents the received speech data.