LOW LATENCY AND MEMORY EFFICIENT KEYWORK SPOTTING

摘要

Features are disclosed for spotting keywords in utterance audio data without requiring the entire utterance to first be processed. Likelihoods that a portion of the utterance audio data corresponds to the keyword may be compared to likelihoods that the portion corresponds to background audio (e.g., general speech and/or non-speech sounds). The difference in the likelihoods may be determined, and keyword may be triggered when the difference exceeds a threshold, or shortly thereafter. Traceback information and other data may be stored during the process so that a second speech processing pass may be performed. For efficient management of system memory, traceback information may only be stored for those frames that may encompass a keyword; the traceback information for older frames may be overwritten by traceback information for newer frames.

主权项

1. A system comprising:
a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least:
obtain a sequence of feature vectors, wherein the sequence of feature vectors represents at least a portion of a stream of audio data;generate a keyword score based at least partly on a likelihood that a particular feature vector of the sequence of feature vectors represents audio data corresponding to a keyword;generate a background score based at least partly on a likelihood that the particular feature vector represents audio data corresponding to background audio;determine that a difference between the keyword score and the background score is greater than differences associated with feature vectors preceding the particular feature vector in a subset of the sequence of feature vectors, wherein the particular feature vector is in a center of the subset;determine that the difference is greater than differences associated with feature vectors subsequent to the particular feature vector in the subset; andgenerate data indicating the particular feature vector corresponds to an end of the keyword.