Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Augmenting Contrastive Learning of Speech Representations in the
  Time Domain   Contrastive Predictive Coding (CPC), based on predicting future segments of
speech based on past segments is emerging as a powerful algorithm for
representation learning of speech signal. However, it still under-performs
other methods on unsupervised evaluation benchmarks. Here, we introduce
WavAugment, a time-domain data augmentation library and find that applying
augmentation in the past is generally more efficient and yields better
performances than other methods. We find that a combination of pitch
modification, additive noise and reverberation substantially increase the
performance of CPC (relative improvement of 18-22%), beating the reference
Libri-light results with 600 times less data. Using an out-of-domain dataset,
time-domain data augmentation can push CPC to be on par with the state of the
art on the Zero Speech Benchmark 2017. We also show that time-domain data
augmentation consistently improves downstream limited-supervision phoneme
classification tasks by a factor of 12-15% relative.

2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
20
The categories of this paper are: acc-phys