Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fully Learnable Front-End for Multi-Channel Acoustic Modeling using
  Semi-Supervised Learning   In this work, we investigated the teacher-student training paradigm to train
a fully learnable multi-channel acoustic model for far-field automatic speech
recognition (ASR). Using a large offline teacher model trained on beamformed
audio, we trained a simpler multi-channel student acoustic model used in the
speech recognition system. For the student, both multi-channel feature
extraction layers and the higher classification layers were jointly trained
using the logits from the teacher model. In our experiments, compared to a
baseline model trained on about 600 hours of transcribed data, a relative
word-error rate (WER) reduction of about 27.3% was achieved when using an
additional 1800 hours of untranscribed data. We also investigated the benefit
of pre-training the multi-channel front end to output the beamformed log-mel
filter bank energies (LFBE) using L2 loss. We find that pre-training improves
the word error rate by 10.7% when compared to a multi-channel model directly
initialized with a beamformer and mel-filter bank coefficients for the front
end. Finally, combining pre-training and teacher-student training produces a
WER reduction of 31% compared to our baseline.

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The categories of this paper are: hep-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an