# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
###### Abstract  
A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system. speaker verification; presentation attack detection; deep neural networks +
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spoofing-aware ISV system. In this paper, PAD refers to replay attacks, because the ASVspoof2017 dataset only focuses on replay attack detection which is known to be the easiest yet effective attack. Three tasks are considered: SV, PAD, and ISV, and performance is evaluated using ZE-EER, PAD-EER, and ISV-EER. While a number of studies have worked to develop independent systems for SV and PAD, few have sought to integrate the SV and PAD systems [12; 13; 14; 15; 16; 17]. More specifically, this handful of studies proposed approaches such as cascaded, parallel [12; 13], and joint systems [14; 16; 17]. Most existing studies used common features to integrate the two tasks for system efficiency. Section 2 further takes up this existing body of work. In this paper, we propose two spoofing-aware frameworks for the ISV task, illustrated in Figure 1. We use a light convolutional neural network (CNN) (LCNN) architecture [18] for both frameworks; this choice is based on its success in various PAD studies [11; 19]. The first proposed framework expands existing work by proposing a monolithic end-to-end (E2E) architecture. More specifically, it conducts speaker identification (SID) and PAD to train a common feature using multi-task learning (MTL) [20]. Concurrently, it uses the embeddings to compose trials and conduct the ISV task. Using the sum of SID, PAD, and ISV losses, the entire DNN is jointly optimized. However, based on tendencies observed during internal experiments, we hypothesize that training a common feature for the ISV task may not be ideal because the properties required for each task differ: the PAD task representation uses device and channel information while SV needs to remove it (further discussed in Section 3). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ZE-EER** & **PAD-EER** & **ISV-EER** \\ \hline SV baseline & 9.58 & 33.72 & 19.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Difference in equal error rate (EER) according to the existence of replay non-target trials. Results demonstrate the vulnerability of speaker verification systems that are unaware of presentation attack detection (PAD).