Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ATCSpeechNet: A multilingual end-to-end speech recognition framework for
  air traffic control systems   In this paper, a multilingual end-to-end framework, called as ATCSpeechNet,
is proposed to tackle the issue of translating communication speech into
human-readable text in air traffic control (ATC) systems. In the proposed
framework, we focus on integrating the multilingual automatic speech
recognition (ASR) into one model, in which an end-to-end paradigm is developed
to convert speech waveform into text directly, without any feature engineering
or lexicon. In order to make up for the deficiency of the handcrafted feature
engineering caused by ATC challenges, a speech representation learning (SRL)
network is proposed to capture robust and discriminative speech representations
from the raw wave. The self-supervised training strategy is adopted to optimize
the SRL network from unlabeled data, and further to predict the speech
features, i.e., wave-to-feature. An end-to-end architecture is improved to
complete the ASR task, in which a grapheme-based modeling unit is applied to
address the multilingual ASR issue. Facing the problem of small transcribed
samples in the ATC domain, an unsupervised approach with mask prediction is
applied to pre-train the backbone network of the ASR model on unlabeled data by
a feature-to-feature process. Finally, by integrating the SRL with ASR, an
end-to-end multilingual ASR framework is formulated in a supervised manner,
which is able to translate the raw wave into text in one model, i.e.,
wave-to-text. Experimental results on the ATCSpeech corpus demonstrate that the
proposed approach achieves a high performance with a very small labeled corpus
and less resource consumption, only 4.20% label error rate on the 58-hour
transcribed corpus. Compared to the baseline model, the proposed approach
obtains over 100% relative performance improvement which can be further
enhanced with the increasing of the size of the transcribed samples.

18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin