  Techniques for multi-lingual and cross-lingual speech recognition can help in
low resource scenarios, to bootstrap systems and enable analysis of new
languages and domains. End-to-end approaches, in particular sequence-based
techniques, are attractive because of their simplicity and elegance. While it
is possible to integrate traditional multi-lingual bottleneck feature
extractors as front-ends, we show that end-to-end multi-lingual training of
sequence models is effective on context independent models trained using
Connectionist Temporal Classification (CTC) loss. We show that our model
improves performance on Babel languages by over 6% absolute in terms of
word/phoneme error rate when compared to mono-lingual systems built in the same
setting for these languages. We also show that the trained model can be adapted
cross-lingually to an unseen language using just 25% of the target data. We
show that training on multiple languages is important for very low resource
cross-lingual target scenarios, but not for multi-lingual testing scenarios.
Here, it appears beneficial to include large well prepared datasets.

Computation and Language;Sound;Audio and Speech Processing