A computer-implemented method comprising: receiving audio data of multiple complete utterances and multiple incomplete utterances; and training, using machine learning, an end of query model that is configured to determine a confidence score that reflects a likelihood that the utterance is a complete utterance, said training using the audio data of the multiple complete utterances and the multiple incomplete utterances, wherein the end of query model is a neural network; receiving audio data that corresponds to an utterance spoken by a user; determining acoustic speech characteristics of the audio data that corresponds to the utterance spoken by the user; applying, to the acoustic speech characteristics, the end of query model; by applying the end of query model, determining the confidence score that reflects a likelihood that the utterance spoken by the user is a complete utterance; comparing the confidence score that reflects the likelihood that the utterance spoken by the user is a complete utterance to a confidence score threshold; based on comparing the confidence score that reflects the likelihood that the utterance spoken by the user is a complete utterance to the confidence score threshold, determining whether the utterance spoken by the user is likely complete when the confidence score satisfies the confidence score threshold or likely incomplete when the confidence score does not satisfy the confidence score threshold; and based on determining whether the utterance spoken by the user is likely complete or likely incomplete, providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance spoken by the user in an active state if it is determined that the utterance is likely incomplete, and maintaining the microphone that is receiving the utterance in the active state or to (ii) deactivate the microphone that is receiving the utterance spoken by the user if it is determined that the utterance spoken by the user is likely complete, and deactivating the microphone that is receiving the utterance spoken by the user. receiving audio data of multiple complete utterances and multiple incomplete utterances; and training, using machine learning, an end of query model that is configured to determine a confidence score that reflects a likelihood that the utterance is a complete utterance, said training using the audio data of the multiple complete utterances and the multiple incomplete utterances, wherein the end of query model is a neural network; receiving audio data that corresponds to an utterance spoken by a user; determining acoustic speech characteristics of the audio data that corresponds to the utterance spoken by the user; applying, to the acoustic speech characteristics, the end of query model; by applying the end of query model, determining the confidence score that reflects a likelihood that the utterance spoken by the user is a complete utterance; comparing the confidence score that reflects the likelihood that the utterance spoken by the user is a complete utterance to a confidence score threshold; based on comparing the confidence score that reflects the likelihood that the utterance spoken by the user is a complete utterance to the confidence score threshold, determining whether the utterance spoken by the user is likely complete when the confidence score satisfies the confidence score threshold or likely incomplete when the confidence score does not satisfy the confidence score threshold; and based on determining whether the utterance spoken by the user is likely complete or likely incomplete, providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance spoken by the user in an active state if it is determined that the utterance is likely incomplete, and maintaining the microphone that is receiving the utterance in the active state or to (ii) deactivate the microphone that is receiving the utterance spoken by the user if it is determined that the utterance spoken by the user is likely complete, and deactivating the microphone that is receiving the utterance spoken by the user.