# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 5 Conclusions  
In this paper, we investigated the integration of speaker verification and presentation attack detection. We proposed two methods for their integration: an E2E monolithic approach and a back-end modular approach. The proposed E2E approach simultaneously trains SID, PAD, and ISV using a common feature. The experimental results of the E2E approach led us to hypothesize that the discriminative information for SID and PAD differs. Based on our hypothesis, we proposed a framework using a separate back-end DNN that takes speaker embedding and a PAD prediction extracted from pretrained SV and PAD systems as input. The effectiveness of our proposed systems was verified using official trials for the ISV task, where we achieved an EER of 15.63%. It is expected that the proposed method will continue to enhance performance when improved speaker embeddings and PAD prediction are input.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **ZE-EER** & **PAD-EER** & **ISV-EER** \\ \hline SV Baseline & 9.58 & 33.72 & 19.98 \\
**\#7-Ours** & 14.32 & **15.46** & **15.63** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the SV baseline, our proposed modular deep neural network (DNN), and other work using the official trials for the ISV task. Numbers in bold represent the best results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Train Loss** & **DNN Arch** & **ZE-EER (SV)** & **PAD-EER** \\ \hline Sid & SV & **9.58** & - \\ Sid+PAD & SV & 17.53 & 13.69 \\ \hline PAD & PAD & - & **10.60** \\ PAD+Sid & PAD & 19.16 & 12.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results showing that the required discriminative information differs for SV and PAD (Sid: speaker identification; PAD: presentation attack detection; Int: integrated speaker verification). Numbers in bold represent the best results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**System** & **ZE-EER (SV)** & **PAD-EER** & **ISV-EER** \\ \hline \#4-w/o mul & 20.52 & 19.77 & 20.48 \\ \#5-w mul & 15.59 & 18.06 & 16.66 \\ \hline \#6-loss weight & 15.22 & **14.55** & 15.91 \\ \#7-DNN arch & **14.32** & 15.46 & **15.63** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the proposed modular approach for the ISV task. Numbers in bold represent the best results.  
**Author Contributions:** Conceptualization, investigation, and writing--original draft preparation and editing, H.-j.S. and J.-w.J.; writing--review and editing, J.-h.K.; supervision, writing--review and editing, H.-j.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science, ICT and Future Planning, Grant number PA-J000001-2017-101.  
**Acknowledgments:** This research was supported by Projects for Research and Development of Police Science and Technology under the Center for Research and Development of Police Science and Technology and the Korean National Police Agency funded by the Ministry of Science, ICT and Future Planning (Grant No. PA-J000001-2017-101).  
**Conflicts of Interest:** The authors declare no conflicts of interest.