Query-by-example keyword spotting using long short-term memory networks

@article{Chen2015QuerybyexampleKS,
  title={Query-by-example keyword spotting using long short-term memory networks},
  author={Guoguo Chen and Carolina Parada and Tara N. Sainath},
  journal={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2015},
  pages={5236-5240}
}
We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model. We use the activations prior to the softmax layer of the LSTM as our keyword-vector. At runtime, we detect the keyword by extracting the same feature vector from a sliding window… CONTINUE READING
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