  In this paper, we propose a context-aware keyword spotting model employing a
character-level recurrent neural network (RNN) for spoken term detection in
continuous speech. The RNN is end-to-end trained with connectionist temporal
classification (CTC) to generate the probabilities of character and
word-boundary labels. There is no need for the phonetic transcription, senone
modeling, or system dictionary in training and testing. Also, keywords can
easily be added and modified by editing the text based keyword list without
retraining the RNN. Moreover, the unidirectional RNN processes an infinitely
long input audio streams without pre-segmentation and keywords are detected
with low-latency before the utterance is finished. Experimental results show
that the proposed keyword spotter significantly outperforms the deep neural
network (DNN) and hidden Markov model (HMM) based keyword-filler model even
with less computations.
