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Many state-of-the-art solutions for the understanding of speech data
have in common to be probabilistic and to rely on machine learning
algorithms to train their models from large amount of data. The
difficulty remains in the cost of collecting and annotating such data.
Another point is the time for updating an existing model to a new domain.
Recent works showed that a zero-shot learning method allows
to bootstrap a model with good initial performance. To do so, this
method relies on exploiting both a small-sized ontological description

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