Abstract: A system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech of a user that is stored on a user's device or the system, which detects the voice activity, to determine according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech, which is based on the user profile, has a high probability of being a prefix of a longer utterance.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A speech-based human-machine interface that parses words spoken to detect a complete parse and, responsive to so detecting, computes a hypothesis as to whether the words are a prefix to another complete parse. The duration of no voice activity period to determine an end of a sentence depends on the prefix hypothesis. The user's typical speech speed profile and a short-term measure of speech speed also scale the period. Speech speed is measured by the time between words, and the period scaling uses a continuously adaptive algorithm. The system uses a longer cut-off period after a system wake-up event but before it detects any voice activity.

Abstract: A speech-based human-machine interface that parses words spoken to detect a complete parse and, responsive to so detecting, computes a hypothesis as to whether the words are a prefix to another complete parse. The duration of no voice activity period to determine an end of a sentence depends on the prefix hypothesis. The user's typical speech speed profile and a short-term measure of speech speed also scale the period. Speech speed is measured by the time between words, and the period scaling uses a continuously adaptive algorithm. The system uses a longer cut-off period after a system wake-up event but before it detects any voice activity.