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@INPROCEEDINGS{Dey_INTERSPEECH2019_2019,
         author = {Dey, Subhadeep and Motlicek, Petr and Bui, Trung and Dernoncourt, Franck},
       projects = {Innosuisse-SM2},
          title = {Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition},
      booktitle = {Proc. of Interspeech 2019},
           year = {2019},
       abstract = {In this paper, we explore various approaches for semi-
supervised learning in an end-to-end automatic speech recog-
nition (ASR) framework. The first step in our approach in-
volves 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 out-
put, further used to retrain the seed model. However, uncer-
tainties 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 hy-
pothesized 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 hy-
pothesized 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.}
}