Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DGC-vector: A new speaker embedding for zero-shot voice conversion   Recently, more and more zero-shot voice conversion algorithms have been
proposed. As a fundamental part of zero-shot voice conversion, speaker
embeddings are the key to improving the converted speech's speaker similarity.
In this paper, we study the impact of speaker embeddings on zero-shot voice
conversion performance. To better represent the characteristics of the target
speaker and improve the speaker similarity in zero-shot voice conversion, we
propose a novel speaker representation method in this paper. Our method
combines the advantages of D-vector, global style token (GST) based speaker
representation and auxiliary supervision. Objective and subjective evaluations
show that the proposed method achieves a decent performance on zero-shot voice
conversion and significantly improves speaker similarity over D-vector and
GST-based speaker embedding.

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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph