Keyword spotting—or wakeword detection—is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection. The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording. Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system. DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud. In this paper, we proposed DONUT, an efficient algorithm for online query-by-example keyword spotting using CTC. The algorithm learns a list of hypothetical label sequences from the user's speech during enrollment and uses these hypotheses to score audios at test time. We showed that the model is interpretable, and thus easy to inspect, debug, and tweak, yet at the same time has high accuracy.Because training a wakeword model amounts to a simple beam search, it is possible to train a model on the user's device without uploading a user's private voice data to the cloud.Our technique is in principle applicable to any domain in which a user would like to teach a system to recognize a sequence of events, such as a melody (a sequence of musical notes) or a gesture (a sequence of hand movements). It would be interesting to see how well the proposed technique transfers to these other domains.
We propose an interpretable model for detecting user-chosen wakewords that learns from the user's examples.
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