Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern
  Architectures   We study pseudo-labeling for the semi-supervised training of ResNet,
Time-Depth Separable ConvNets, and Transformers for speech recognition, with
either CTC or Seq2Seq loss functions. We perform experiments on the standard
LibriSpeech dataset, and leverage additional unlabeled data from LibriVox
through pseudo-labeling. We show that while Transformer-based acoustic models
have superior performance with the supervised dataset alone, semi-supervision
improves all models across architectures and loss functions and bridges much of
the performance gaps between them. In doing so, we reach a new state-of-the-art
for end-to-end acoustic models decoded with an external language model in the
standard supervised learning setting, and a new absolute state-of-the-art with
semi-supervised training. Finally, we study the effect of leveraging different
amounts of unlabeled audio, propose several ways of evaluating the
characteristics of unlabeled audio which improve acoustic modeling, and show
that acoustic models trained with more audio rely less on external language
models.

21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex