Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining query refinements using search data. In one aspect, a method includes receiving a first query and a second query each comprising one or more n-grams for a user session, determining a first set of query refinements for the first query, determining a second set of query refinements from the first set of query refinements, each query refinement in the second set of query refinements including at least one n-gram that is similar to an n-gram from the first query and at least on n-gram that is similar to an n-gram from the second query, scoring each query refinement in the second set of query refinements, selecting a third query from a group consisting of the second set of query refinements and the second query, and providing the third query as input to a search operation.

Abstract: Implementations relate to correcting a speech recognition hypothesis based on prior correction(s) made by a user and/or fulfillment data associated with fulfilling a request embodied in the speech recognition hypothesis. A candidate speech recognition hypothesis can be generated in response to the user providing a spoken utterance to an application, such as an automated assistant. When a confidence metric for the candidate speech recognition hypothesis does not satisfy a threshold, one or more terms of the candidate speech recognition hypothesis can be compared to correcting data. The correcting data can indicate whether the user previously corrected any term(s) present in the candidate speech recognition hypothesis and, if so, correct the term(s) accordingly. Fulfillment data generated for the candidate hypothesis and/or for the corrected hypothesis can also be processed to determine whether to utilize the candidate hypothesis or the corrected hypothesis in responding to the user.

Abstract: Implementations relate to correcting a speech recognition hypothesis based on prior correction(s) made by a user and/or fulfillment data associated with fulfilling a request embodied in the speech recognition hypothesis. A candidate speech recognition hypothesis can be generated in response to the user providing a spoken utterance to an application, such as an automated assistant. When a confidence metric for the candidate speech recognition hypothesis does not satisfy a threshold, one or more terms of the candidate speech recognition hypothesis can be compared to correcting data. The correcting data can indicate whether the user previously corrected any term(s) present in the candidate speech recognition hypothesis and, if so, correct the term(s) accordingly. Fulfillment data generated for the candidate hypothesis and/or for the corrected hypothesis can also be processed to determine whether to utilize the candidate hypothesis or the corrected hypothesis in responding to the user.

Abstract: A method includes receiving follow-on audio data captured by an assistant-enabled device, the follow-on audio data corresponding to a follow-on query spoken by a user of the assistant-enabled device to a digital assistant subsequent to the user submitting a previous query to the digital assistant. The method also includes processing, using a speech recognizer, the follow-on audio data to generate multiple candidate hypotheses, each candidate hypothesis corresponding to a candidate transcription for the follow-on query and represented by a respective sequence of hypothesized terms. For each corresponding candidate hypothesis among the multiple candidate hypotheses, the method also includes determining a corresponding similarity metric between the previous query and the corresponding candidate hypothesis and determining a transcription of the follow-on query spoken by the user based on the similarity metrics determined for the multiple candidate hypotheses.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining query refinements using search data. In one aspect, a method includes receiving a first query and a second query each comprising one or more n-grams for a user session, determining a first set of query refinements for the first query, determining a second set of query refinements from the first set of query refinements, each query refinement in the second set of query refinements including at least one n-gram that is similar to an n-gram from the first query and at least on n-gram that is similar to an n-gram from the second query, scoring each query refinement in the second set of query refinements, selecting a third query from a group consisting of the second set of query refinements and the second query, and providing the third query as input to a search operation.

Abstract: A method includes receiving follow-on audio data captured by an assistant-enabled device, the follow-on audio data corresponding to a follow-on query spoken by a user of the assistant-enabled device to a digital assistant subsequent to the user submitting a previous query to the digital assistant. The method also includes processing, using a speech recognizer, the follow-on audio data to generate multiple candidate hypotheses, each candidate hypothesis corresponding to a candidate transcription for the follow-on query and represented by a respective sequence of hypothesized terms. For each corresponding candidate hypothesis among the multiple candidate hypotheses, the method also includes determining a corresponding similarity metric between the previous query and the corresponding candidate hypothesis and determining a transcription of the follow-on query spoken by the user based on the similarity metrics determined for the multiple candidate hypotheses.

Abstract: Implementations relate to correcting a speech recognition hypothesis based on prior correction(s) made by a user and/or fulfillment data associated with fulfilling a request embodied in the speech recognition hypothesis. A candidate speech recognition hypothesis can be generated in response to the user providing a spoken utterance to an application, such as an automated assistant. When a confidence metric for the candidate speech recognition hypothesis does not satisfy a threshold, one or more terms of the candidate speech recognition hypothesis can be compared to correcting data. The correcting data can indicate whether the user previously corrected any term(s) present in the candidate speech recognition hypothesis and, if so, correct the term(s) accordingly. Fulfillment data generated for the candidate hypothesis and/or for the corrected hypothesis can also be processed to determine whether to utilize the candidate hypothesis or the corrected hypothesis in responding to the user.