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We define salient features as features that are shared by signals that aredefined as being equivalent by a system designer. The definition allows thedesigner to contribute qualitative information. We aim to find salient featuresthat are useful as conditioning for generative networks. We extract salientfeatures by jointly training a set of clones of an encoder network. Eachnetwork clone receives as input a different signal from a set of equivalentsignals. The objective function encourages the network clones to map theirinput into a set of features that is identical across the clones. Itadditionally encourages feature independence and, optionally, reconstruction ofa desired target signal by a decoder. As an application, we train a system thatextracts a time-sequence of feature vectors of speech and uses it as aconditioning of a WaveNet generative system, facilitating both coding andenhancement.

Salient Speech Representations Based on Cloned Networks