Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels

Shlomo Salo Elia, Aviad Malachi, Vered Aharonson, Gadi Pinkas
6/9/2024
cs.SDcs.LGeess.AS

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Abstract

Domain adaptation is often hampered by exceedingly small target datasets and inaccessible source data. These conditions are prevalent in speech verification, where privacy policies and/or languages with scarce speech resources limit the availability of sufficient data. This paper explored techniques of sourcefree domain adaptation unto a limited target speech dataset for speaker verificationin data-scarce languages. Both language and channel mis-match between source and target were investigated. Fine-tuning methods were evaluated and compared across different sizes of labeled target data. A novel iterative cluster-learn algorithm was studied for unlabeled target datasets.

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Cite this paper

@article{elia2024source,
  title  = {Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels},
  author = {Shlomo Salo Elia and Aviad Malachi and Vered Aharonson and Gadi Pinkas},
  year   = {2024},
  eprint = {2406.05863},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.05863v1}
}

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