A speech recognition apparatus and an operating method thereof which execute a mounted artificial intelligence (AI) algorithm and/or machine learning algorithm to perform speech recognition and communicate with different electronic apparatuses and external servers in a 5G communication environment are disclosed. A speech recognition method according to an exemplary embodiment of the present disclosure includes determining a temporary pause for reception of a first utterance sentence in the middle of the reception of the first utterance sentence, outputting a speech recognition processing result of a second utterance sentence which is received after the temporary pause, separately from the first utterance sentence, determining a third utterance sentence which is received after outputting the speech recognition processing result of the second utterance sentence as an extension of the first utterance sentence, and outputting a speech recognition processing result of a fourth utterance sentence obtained by combining the first utterance sentence and the third utterance sentence. According to the present disclosure, a delay occurring in the middle of reception of uttering speech is recognized as an uncompleted utterance to be temporarily stored and a speech recognition processing result for an additional uttering speech received after the delay is provided and then uttering speech which is input again and the uttering speech before the delay are recognized as completed utterance and a speech recognition processing result is provided to improve the speech recognition processing performance.

The method can include: determining sensor information with an aircraft sensor suite; based on the sensor information, determining a flight command using a set of models; validating the flight command S130; and facilitating execution of a validated flight command. The method can optionally include generating a trained model. However, the method S100 can additionally or alternatively include any other suitable elements. The method can function to facilitate aircraft control based on autonomously generated flight commands. The method can additionally or alternatively function to achieve human-in-the-loop autonomous aircraft control, and/or can function to generate a trained neural network based on validation of autonomously generated aircraft flight commands.

The method can include: determining sensor information with an aircraft sensor suite; based on the sensor information, determining a flight command using a set of models; validating the flight command S130; and facilitating execution of a validated flight command. The method can optionally include generating a trained model. However, the method S100 can additionally or alternatively include any other suitable elements. The method can function to facilitate aircraft control based on autonomously generated flight commands. The method can additionally or alternatively function to achieve human-in-the-loop autonomous aircraft control, and/or can function to generate a trained neural network based on validation of autonomously generated aircraft flight commands.

A computing system obtains features that have been extracted from an acoustic signal, where the acoustic signal comprises spoken words uttered by a user. The computing system performs automatic speech recognition (ASR) based upon the features and a language model (LM) generated based upon expanded pattern data. The expanded pattern data includes a name of an entity and a search term, where the entity belongs to a segment identified in a knowledge base. The search term has been included in queries for entities belonging to the segment. The computing system identifies a sequence of words corresponding to the features based upon results of the ASR. The computing system transmits computer-readable text to a search engine, where the text includes the sequence of words.

Methods, apparatus, systems, and computer-readable media for engaging an automated assistant to perform multiple tasks through a multitask command. The multitask command can be a command that, when provided by a user, causes the automated assistant to invoke multiple different agent modules for performing tasks to complete the multitask command. During execution of the multitask command, a user can provide input that can be used by one or more agent modules to perform their respective tasks. Furthermore, feedback from one or more agent modules can be used by the automated assistant to dynamically alter tasks in order to more effectively use resources available during completion of the multitask command.

Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.

Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.

Provided is a device control system configured to: acquire user setting relating to a device; generate a phrase for controlling the device based on the acquired user setting; and output data for displaying the generated phrase.

Provided is a device control system configured to: acquire user setting relating to a device; generate a phrase for controlling the device based on the acquired user setting; and output data for displaying the generated phrase.

Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.