A framework of text-dependent speaker verification for chinese numerical string corpus
<|reference_start|>A framework of text-dependent speaker verification for chinese numerical string corpus: The Chinese numerical string corpus, serves as a valuable resource for speaker verification, particularly in financial transactions. Researches indicate that in short speech scenarios, text-dependent speaker verification (TD-SV) consistently outperforms text-independent speaker verification (TI-SV). However, TD-SV potentially includes the validation of text information, that can be negatively impacted by reading rhythms and pauses. To address this problem, we propose an end-to-end speaker verification system that enhances TD-SV by decoupling speaker and text information. Our system consists of a text embedding extractor, a speaker embedding extractor and a fusion module. In the text embedding extractor, we employ an enhanced Transformer and introduce a triple loss including text classification loss, connectionist temporal classification (CTC) loss and decoder loss; while in the speaker embedding extractor, we create a multi-scale pooling method by combining sliding window attentive statistics pooling (SWASP) with attentive statistics pooling (ASP). To mitigate the scarcity of data, we have recorded a publicly available Chinese numerical corpus named SHALCAS22A (hereinafter called SHAL), which can be accessed on Open-SLR. Moreover, we employ data augmentation techniques using Tacotron2 and HiFi-GAN. Our method achieves an equal error rate (EER) performance improvement of 49.2% on Hi-Mia and 75.0% on SHAL, respectively.<|reference_end|>
@article{zheng2024a,
    title={A framework of text-dependent speaker verification for chinese numerical
  string corpus},
    author={Litong Zheng, Feng Hong, Weijie Xu, Wan Zheng},
    journal={arXiv preprint arXiv:2405.07029},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07029},
    primaryClass={cs.SD eess.AS}
}