Title: Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language,
Abstract: We address the problem of efficient acoustic-model refinement (continuous
retraining) using semi-supervised and active learning for a low resource Indian
language, wherein the low resource constraints are having i) a small labeled
corpus from which to train a baseline `seed' acoustic model and ii) a large
training corpus without orthographic labeling or from which to perform a data
selection for manual labeling at low costs. The proposed semi-supervised
learning decodes the unlabeled large training corpus using the seed model and
through various protocols, selects the decoded utterances with high reliability
using confidence levels (that correlate to the WER of the decoded utterances)
and iterative bootstrapping. The proposed active learning protocol uses
confidence level based metric to select the decoded utterances from the large
unlabeled corpus for further labeling. The semi-supervised learning protocols
can offer a WER reduction, from a poorly trained seed model, by as much as 50%
of the best WER-reduction realizable from the seed model's WER, if the large
corpus were labeled and used for acoustic-model training. The active learning
protocols allow that only 60% of the entire training corpus be manually
labeled, to reach the same performance as the entire data.