Abstract: Implementations relate to determining a language for speech recognition of a spoken utterance, received via an automated assistant interface, for interacting with an automated assistant. In various implementations, audio data indicative of a voice input that includes a natural language request from a user may be applied as input across multiple speech-to-text (“STT”) machine learning models to generate multiple candidate speech recognition outputs. Each STT machine learning model may trained in a particular language. For each respective STT machine learning model of the multiple STT models, the multiple candidate speech recognition outputs may be analyzed to determine an entropy score for the respective STT machine learning model. Based on the entropy scores, a target language associated with at least one STT machine learning model of the multiple STT machine learning models may be selected. The automated assistant may respond to the request using the target language.

Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.

Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.

Abstract: Implementations relate to determining a language for speech recognition of a spoken utterance, received via an automated assistant interface, for interacting with an automated assistant. In various implementations, audio data indicative of a voice input that includes a natural language request from a user may be applied as input across multiple speech-to-text (“STT”) machine learning models to generate multiple candidate speech recognition outputs. Each STT machine learning model may trained in a particular language. For each respective STT machine learning model of the multiple STT models, the multiple candidate speech recognition outputs may be analyzed to determine an entropy score for the respective STT machine learning model. Based on the entropy scores, a target language associated with at least one STT machine learning model of the multiple STT machine learning models may be selected. The automated assistant may respond to the request using the target language.

Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.