  Researches indicate that text-dependent speaker verification (TD-SV) often
outperforms text-independent verification (TI-SV) in short speech scenarios.
However, collecting large-scale fixed text speech data is challenging, and as
speech length increases, factors like sentence rhythm and pauses affect TDSV's
sensitivity to text sequence. Based on these factors, We propose the hypothesis
that strategies such as more fine-grained pooling methods on time scales and
decoupled representations of speech speaker embedding and text embedding are
more suitable for TD-SV. We have introduced an end-to-end TD-SV system based on
a dataset comprising longer Chinese numerical string texts. It contains a text
embedding network, a speaker embedding network, and back-end fusion. First, we
recorded a dataset consisting of long Chinese numerical text named SHAL, which
is publicly available on the Open-SLR website. We addressed the issue of
dataset scarcity by augmenting it using Tacotron2 and HiFi-GAN. Next, we
introduced a dual representation of speech with text embedding and speaker
embedding. In the text embedding network, we employed an enhanced Transformer
and introduced a triple loss that includes text classification loss, CTC loss,
and decoder loss. For the speaker embedding network, we enhanced a sliding
window attentive statistics pooling (SWASP), combined with attentive statistics
pooling (ASP) to create a multi-scale pooling method. Finally, we fused text
embedding and speaker embedding. Our pooling methods achieved an equal error
rate (EER) performance improvement of 49.2% on Hi-Mia and 75.0% on SHAL,
respectively.
