Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autoregressive Co-Training for Learning Discrete Speech Representations   While several self-supervised approaches for learning discrete speech
representation have been proposed, it is unclear how these seemingly similar
approaches relate to each other. In this paper, we consider a generative model
with discrete latent variables that learns a discrete representation for
speech. The objective of learning the generative model is formulated as
information-theoretic co-training. Besides the wide generality, the objective
can be optimized with several approaches, subsuming HuBERT-like training and
vector quantization for learning discrete representation. Empirically, we find
that the proposed approach learns discrete representation that is highly
correlated with phonetic units, more correlated than HuBERT-like training and
vector quantization.

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The categories of this paper are: nlin
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg