Detection and/or enrollment of hot commands to trigger responsive action by automated assistant

A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to implement an automated assistant, wherein the automated assistant is to:

• receive audio data captured at one or more microphones, wherein the audio data includes first and second spoken utterances;
• perform speech recognition processing on the audio data to generate first and second textual snippets corresponding to the first and second spoken utterances;
• apply the first and second textual snippets as inputs across a machine learning model to generate first and second outputs, wherein the first and second outputs are indicative of first and second probabilities, respectively, that the first and second textual snippets are directed to the automated assistant;
• in response to a determination, based on the first output that the first textual snippet satisfies one or more hot command criteria, perform a responsive action based on the first textual snippet;
• in response to a determination, based on the second output, that the second textual snippet fails to satisfy the one or more hot command criteria, discard or disregard the second textual snippet without performing another responsive action.

At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:

• operating an automated assistant using one or more of the processors;
• transitioning the automated assistant from a limited listening state into a full speech recognition state in response to a trigger event;
• while in the full speech recognition state, receiving, by the automated assistant, a spoken command from a user;
• performing speech recognition processing on the spoken command to generate a textual command;
• apply the textual command as input across a machine learning model to generate output, wherein the output is indicative of a probability that the textual command was directed to the automated assistant;
• determining that the textual command satisfies a frequency threshold in a corpus of textual commands;
• and
• based on the output and the determining, enrolling data indicative of the textual command as a hot command, wherein subsequent to the enrolling, utterance of another textual command that is semantically consistent with the textual command triggers performance of a responsive action by the automated assistant, without requiring explicit invocation of the automated assistant.