A method (700, 1000) comprising: receiving, via a microphone of a head-wearable device, an audio signal, wherein the audio signal comprises voice activity; receiving, via one or more sensors (128A, 128B, 444) of the head-wearable device, non-verbal sensor data, the one or more sensors (128A, 128B, 444) not including the microphone, the non-verbal sensor data corresponding to a user of the head-wearable device; classifying, via a first classifier, audio data corresponding to the audio signal; determining whether the audio signal comprises a pause in the voice activity; responsive to determining that the audio signal comprises a pause in the voice activity, determining, based on the classifying of the audio data, whether the pause in the voice activity corresponds to an end point of the voice activity; and responsive to determining that the pause in the voice activity corresponds to an end point of the voice activity, presenting a response to a user based on the voice activity, wherein determining whether the pause in the voice activity corresponds to an end point of the voice activity comprises: determining a probability of interest based on the classifying of the audio data and based further on applying the non-verbal sensor data as input to a machine learning process, determining whether the probability of interest exceeds a threshold, and in accordance with a determining that the probability of interest exceeds the threshold, determining that the pause in the voice activity corresponds to an end point of the voice activity. receiving, via a microphone of a head-wearable device, an audio signal, wherein the audio signal comprises voice activity; receiving, via one or more sensors (128A, 128B, 444) of the head-wearable device, non-verbal sensor data, the one or more sensors (128A, 128B, 444) not including the microphone, the non-verbal sensor data corresponding to a user of the head-wearable device; classifying, via a first classifier, audio data corresponding to the audio signal; determining whether the audio signal comprises a pause in the voice activity; responsive to determining that the audio signal comprises a pause in the voice activity, determining, based on the classifying of the audio data, whether the pause in the voice activity corresponds to an end point of the voice activity; and responsive to determining that the pause in the voice activity corresponds to an end point of the voice activity, presenting a response to a user based on the voice activity, wherein determining whether the pause in the voice activity corresponds to an end point of the voice activity comprises: determining a probability of interest based on the classifying of the audio data and based further on applying the non-verbal sensor data as input to a machine learning process, determining whether the probability of interest exceeds a threshold, and in accordance with a determining that the probability of interest exceeds the threshold, determining that the pause in the voice activity corresponds to an end point of the voice activity. determining a probability of interest based on the classifying of the audio data and based further on applying the non-verbal sensor data as input to a machine learning process, determining whether the probability of interest exceeds a threshold, and in accordance with a determining that the probability of interest exceeds the threshold, determining that the pause in the voice activity corresponds to an end point of the voice activity.