  In this paper, we propose a spoken term detection algorithm for simultaneous
prediction and localization of in-vocabulary and out-of-vocabulary terms within
an audio segment. The proposed algorithm infers whether a term was uttered
within a given speech signal or not by predicting the word embeddings of
various parts of the speech signal and comparing them to the word embedding of
the desired term. The algorithm utilizes an existing embedding space for this
task and does not need to train a task-specific embedding space. At inference
the algorithm simultaneously predicts all possible locations of the target term
and does not need dynamic programming for optimal search. We evaluate our
system on several spoken term detection tasks on read speech corpora.
