Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequence-based Multi-lingual Low Resource Speech Recognition   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.

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The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an