Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stream attention-based multi-array end-to-end speech recognition   Automatic Speech Recognition (ASR) using multiple microphone arrays has
achieved great success in the far-field robustness. Taking advantage of all the
information that each array shares and contributes is crucial in this task.
Motivated by the advances of joint Connectionist Temporal Classification
(CTC)/attention mechanism in the End-to-End (E2E) ASR, a stream attention-based
multi-array framework is proposed in this work. Microphone arrays, acting as
information streams, are activated by separate encoders and decoded under the
instruction of both CTC and attention networks. In terms of attention, a
hierarchical structure is adopted. On top of the regular attention networks,
stream attention is introduced to steer the decoder toward the most informative
encoders. Experiments have been conducted on AMI and DIRHA multi-array corpora
using the encoder-decoder architecture. Compared with the best single-array
results, the proposed framework has achieved relative Word Error Rates (WERs)
reduction of 3.7% and 9.7% in the two datasets, respectively, which is better
than conventional strategies as well.

18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom