  This paper describes our submission to Task 1 of the Short-duration Speaker
Verification (SdSV) challenge 2020. Task 1 is a text-dependent speaker
verification task, where both the speaker and phrase are required to be
verified. The submitted systems were composed of TDNN-based and ResNet-based
front-end architectures, in which the frame-level features were aggregated with
various pooling methods (e.g., statistical, self-attentive, ghostVLAD pooling).
Although the conventional pooling methods provide embeddings with a sufficient
amount of speaker-dependent information, our experiments show that these
embeddings often lack phrase-dependent information. To mitigate this problem,
we propose a new pooling and score compensation methods that leverage a
CTC-based automatic speech recognition (ASR) model for taking the lexical
content into account. Both methods showed improvement over the conventional
techniques, and the best performance was achieved by fusing all the
experimented systems, which showed 0.0785% MinDCF and 2.23% EER on the
challenge's evaluation subset.
