  Learning good representations is of crucial importance in deep learning.
Mutual Information (MI) or similar measures of statistical dependence are
promising tools for learning these representations in an unsupervised way. Even
though the mutual information between two random variables is hard to measure
directly in high dimensional spaces, some recent studies have shown that an
implicit optimization of MI can be achieved with an encoder-discriminator
architecture similar to that of Generative Adversarial Networks (GANs). In this
work, we learn representations that capture speaker identities by maximizing
the mutual information between the encoded representations of chunks of speech
randomly sampled from the same sentence. The proposed encoder relies on the
SincNet architecture and transforms raw speech waveform into a compact feature
vector. The discriminator is fed by either positive samples (of the joint
distribution of encoded chunks) or negative samples (from the product of the
marginals) and is trained to separate them. We report experiments showing that
this approach effectively learns useful speaker representations, leading to
promising results on speaker identification and verification tasks. Our
experiments consider both unsupervised and semi-supervised settings and compare
the performance achieved with different objective functions.
