  We explore a keyword-based spoken language understanding system, in which the
intent of the user can directly be derived from the detection of a sequence of
keywords in the query. In this paper, we focus on an open-vocabulary keyword
spotting method, allowing the user to define their own keywords without having
to retrain the whole model. We describe the different design choices leading to
a fast and small-footprint system, able to run on tiny devices, for any
arbitrary set of user-defined keywords, without training data specific to those
keywords. The model, based on a quantized long short-term memory (LSTM) neural
network, trained with connectionist temporal classification (CTC), weighs less
than 500KB. Our approach takes advantage of some properties of the predictions
of CTC-trained networks to calibrate the confidence scores and implement a fast
detection algorithm. The proposed system outperforms a standard keyword-filler
model approach.

Computation and Language