  Discovering speaker independent acoustic units purely from spoken input is
known to be a hard problem. In this work we propose an unsupervised speaker
normalization technique prior to unit discovery. It is based on separating
speaker related from content induced variations in a speech signal with an
adversarial contrastive predictive coding approach. This technique does neither
require transcribed speech nor speaker labels, and, furthermore, can be trained
in a multilingual fashion, thus achieving speaker normalization even if only
few unlabeled data is available from the target language. The speaker
normalization is done by mapping all utterances to a medoid style which is
representative for the whole database. We demonstrate the effectiveness of the
approach by conducting acoustic unit discovery with a hidden Markov model
variational autoencoder noting, however, that the proposed speaker
normalization can serve as a front end to any unit discovery system.
Experiments on English, Yoruba and Mboshi show improvements compared to using
non-normalized input.
