Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer learning of language-independent end-to-end ASR with language
  model fusion   This work explores better adaptation methods to low-resource languages using
an external language model (LM) under the framework of transfer learning. We
first build a language-independent ASR system in a unified sequence-to-sequence
(S2S) architecture with a shared vocabulary among all languages. During
adaptation, we perform LM fusion transfer, where an external LM is integrated
into the decoder network of the attention-based S2S model in the whole
adaptation stage, to effectively incorporate linguistic context of the target
language. We also investigate various seed models for transfer learning.
Experimental evaluations using the IARPA BABEL data set show that LM fusion
transfer improves performances on all target five languages compared with
simple transfer learning when the external text data is available. Our final
system drastically reduces the performance gap from the hybrid systems.

11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph