  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.
