Title: DONUT: CTC-based Query-by-Example Keyword Spotting,
Abstract: 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.