# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 5 Experimental Results  
In this section, we present experimental results for the short-duration gender-independent as well as gender-dependent ASV tasks: the 1 s-1 s-GD and 1 s-1 s-GI tasks as well as the 3 s-3 s-GD and 3 s-3 s-GI tasks. The performances are reported in terms of equal error rate (EER) [1], a verification error measure that gives the accuracy at decision threshold for which the probabilities of false rejection (miss) and false acceptance (false alarm) are equal. The probability of false rejection is the ratio of the number of false rejection (target speaker is misclassified as non-target speaker) divided by the total number of target test trials. The probability of false acceptance is the ratio of the number of false acceptance (non-target speaker is misclassified as target speaker) divided by the total number of non-target test trials. In Table 3, we examine the difference between Mandarin singing and normal reading speech for ASV 1 s-1 s task using three different systems, the conventional GMM-based, DTW-based and the state-of-the-art DNN X-vector based systems. From preliminary results in Table 3, it is clear that the GMM-based and DTW-based systems achieved almost the same overall performances. Moreover, we  
Figure 4: Diagram of the X-vector extractor. found that the GMM and DTW based systems obtained big EER difference between using singing and reading speech, both for the gender-independent trials and the female gender-dependent trials. These preliminary results show that the voiceprint information extracted from the singing speech was more discriminative than the one extracted from reading speech, especially for the female speaker discrimination. By comparing EERs obtained from singing and reading speech on gender-dependent tasks, we obtained a relative 23.2% EER reduction for female trials. For gender-independent task, a relative 26.1% EER reduction was obtained for the overall test trials. In addition, compared with GMM-based and DTW-based systems, the state-of-the-art X-vector based system achieved much better overall results on the \"GD\" and \"GI\" tasks. However, we can see that the X-vector based system obtained the exact same results for both the ASV tasks using reading and singing speech. From the X-vector based systems, we did not see any performance difference between using these two different speaking styles. The main reason is that both the model training of X-vector extractor and PLDA backend need a large amount of labeled data. In this study, they were trained using the out-of-domain \"AISHELL\" corpus with only reading style speech because the RSS corpus is very limited. As mentioned in the X-vector based system description in Section 4, the RSS corpus cannot be used for X-vector extractor training in general. The acoustic properties learned by the extractor and PLDA deviated far from the singing speech; they were biased to the reading speech. The biased models make the speaker discrimination of singing incapable of being reflected. We will re-validate the X-vector based systems in our future works, when enough singing speech samples are available to train an unbiased deep X-vector extractor. In fact, we used the X-vector based system in this study to only show preliminary results for speaker verification based on singing speech, even the training data of X-vector extractor was biased to the reading-speech. Therefore, it is unfair to compare the X-vector based system with the conventional GMM-based and DTW-based systems, because the amount of data we used for X-vector based system was much higher than the amount we used for the GMM-based and DTW-based system (only using the target speaker enrollment speech for model training), and this is the main reason for the better performance on some conditions in Table 3.  
In our experiments on 3 s-3 s tasks, it is interesting to observe that all of the GMM-based, DTW-based and X-vector based systems obtained zero EERs on both the 3 s-3 s-GD and 3 s-3 s-GI ASV tasks. That is, all of the test trials were correctly detected.