  End-to-end (E2E) models have been explored for large speech corpora and have
been found to match or outperform traditional pipeline-based systems in some
languages. However, most prior work on end-to-end models use speech corpora
exceeding hundreds or thousands of hours. In this study, we explore end-to-end
models for code-switched Hindi-English language with less than 50 hours of
data. We utilize two specific measures to improve network performance in the
low-resource setting, namely multi-task learning (MTL) and balancing the corpus
to deal with the inherent class imbalance problem i.e. the skewed frequency
distribution over graphemes. We compare the results of the proposed approaches
with traditional, cascaded ASR systems. While the lack of data adversely
affects the performance of end-to-end models, we see promising improvements
with MTL and balancing the corpus.
