Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Speech Representation Learning Combining Conformer CPC with Deep Cluster
  for the ZeroSpeech Challenge 2021   We present a system for the Zero Resource Speech Challenge 2021, which
combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep
cluster, we first prepare pseudo-labels obtained by clustering the outputs of a
CPC network with k-means. Then, we train an additional autoregressive model to
classify the previously obtained pseudo-labels in a supervised manner. Phoneme
discriminative representation is achieved by executing the second-round
clustering with the outputs of the final layer of the autoregressive model. We
show that replacing a Transformer layer with a Conformer layer leads to a
further gain in a lexical metric. Experimental results show that a relative
improvement of 35% in a phonetic metric, 1.5% in the lexical metric, and 2.3%
in a syntactic metric are achieved compared to a baseline method of CPC-small
which is trained on LibriSpeech 460h data. We achieve top results in this
challenge with the syntactic metric.

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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph