  Transcribed datasets typically contain speaker identity for each instance in
the data. We investigate two ways to incorporate this information during
training: Multi-Task Learning and Adversarial Learning. In multi-task learning,
the goal is speaker prediction; we expect a performance improvement with this
joint training if the two tasks of speech recognition and speaker recognition
share a common set of underlying features. In contrast, adversarial learning is
a means to learn representations invariant to the speaker. We then expect
better performance if this learnt invariance helps generalizing to new
speakers. While the two approaches seem natural in the context of speech
recognition, they are incompatible because they correspond to opposite
gradients back-propagated to the model. In order to better understand the
effect of these approaches in terms of error rates, we compare both strategies
in controlled settings. Moreover, we explore the use of additional
untranscribed data in a semi-supervised, adversarial learning manner to improve
error rates. Our results show that deep models trained on big datasets already
develop invariant representations to speakers without any auxiliary loss. When
considering adversarial learning and multi-task learning, the impact on the
acoustic model seems minor. However, models trained in a semi-supervised manner
can improve error-rates.
