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@INPROCEEDINGS{Keshet_NOLISP_2007,
         author = {Keshet, Joseph and Grangier, David and Bengio, Samy},
       projects = {Idiap, DIRAC},
          title = {Discriminative Keyword Spotting},
      booktitle = {Workshop on Non-Linear Speech Processing},
           year = {2007},
       location = {Paris, France},
       abstract = {This paper proposes a new approach for keyword spotting, which is
not based on HMMs. The proposed method employs a new discriminative learning
procedure, in which the learning phase aims at maximizing the area under the
ROC curve, as this quantity is the most common measure to evaluate keyword
spotters. The keyword spotter we devise is based on non-linearly mapping the input
acoustic representation of the speech utterance along with the target keyword into
an abstract vector space. Building on techniques used for large margin methods for
predicting whole sequences, our keyword spotter distills to a classifier in the
abstract vector-space which separates speech utterances in which the keyword is
uttered from speech utterances in which the keyword is not uttered. We describe a
simple iterative algorithm for learning the keyword spotter and discuss its formal
properties. Experiments with the TIMIT corpus show that our method outperforms the
conventional HMM-based approach.},
            pdf = {https://publications.idiap.ch/attachments/papers/2008/Keshet_NOLISP_2007.pdf}
}