Robust Text-Dependent Speaker Verification via Character-Level Information Preservation for the SdSV Challenge 2020

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.