  Despite the growing interest in unsupervised learning, extracting meaningful
knowledge from unlabelled audio remains an open challenge. To take a step in
this direction, we recently proposed a problem-agnostic speech encoder (PASE),
that combines a convolutional encoder followed by multiple neural networks,
called workers, tasked to solve self-supervised problems (i.e., ones that do
not require manual annotations as ground truth). PASE was shown to capture
relevant speech information, including speaker voice-print and phonemes. This
paper proposes PASE+, an improved version of PASE for robust speech recognition
in noisy and reverberant environments. To this end, we employ an online speech
distortion module, that contaminates the input signals with a variety of random
disturbances. We then propose a revised encoder that better learns short- and
long-term speech dynamics with an efficient combination of recurrent and
convolutional networks. Finally, we refine the set of workers used in
self-supervision to encourage better cooperation. Results on TIMIT, DIRHA and
CHiME-5 show that PASE+ significantly outperforms both the previous version of
PASE as well as common acoustic features. Interestingly, PASE+ learns
transferable representations suitable for highly mismatched acoustic
conditions.
