Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels
<|reference_start|>Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels: 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.<|reference_end|>
@article{elia2024source,
    title={Source -Free Domain Adaptation for Speaker Verification in Data-Scarce
  Languages and Noisy Channels},
    author={Shlomo Salo Elia, Aviad Malachi, Vered Aharonson, Gadi Pinkas},
    journal={arXiv preprint arXiv:2406.05863},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05863},
    primaryClass={cs.SD cs.LG eess.AS}
}