  In this paper, we explore various approaches for semi supervised learning in
an end to end automatic speech recognition (ASR) framework. The first step in
our approach involves training a seed model on the limited amount of labelled
data. Additional unlabelled speech data is employed through a data selection
mechanism to obtain the best hypothesized output, further used to retrain the
seed model. However, uncertainties of the model may not be well captured with a
single hypothesis. As opposed to this technique, we apply a dropout mechanism
to capture the uncertainty by obtaining multiple hypothesized text transcripts
of an speech recording. We assume that the diversity of automatically generated
transcripts for an utterance will implicitly increase the reliability of the
model. Finally, the data selection process is also applied on these
hypothesized transcripts to reduce the uncertainty. Experiments on freely
available TEDLIUM corpus and proprietary Adobe's internal dataset show that the
proposed approach significantly reduces ASR errors, compared to the baseline
model.
