Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification
Table 2: EER(%) and DCF16 of SRE16 Evaluation Dataset
['System Description ID', 'System Description system', 'System Description triplet', 'Pool EER(%)', 'Pool DCF16', 'Tagalog EER(%)', 'Tagalog DCF16', 'Cantonese EER(%)', 'Cantonese DCF16']
We report the results in terms of EER and the SRE16 official evaluation metric DCF16, which is averaged from two operation points with PTarget=0.01 and PTarget=0.005 respectively. similarity measurement network outperform the baseline x-vector system that only trained with softmax loss. We first investigate the influence of a single improvement. The two methods show their superiorities on different evaluation indicators. More specifically, jointly training with embedding similarity measurement network (System 3) can reduce DCF obviously with a little effect of EER. At the same time, jointly training with triplet distance (System 4) remarkably decreases EER. The observation is that the triplet distance loss shows reduced within-speaker variance, while the similarity measurement network shows its superior property on between-speaker distance. The triplet distance can achieve better performance when the miss rate is very low, while the similarity measurement network performs better when the false alarm rate is low, and the performance of joint training is slightly better than the triplet distance. Combining these factors, it is proved that the embedding similarity measurement network can truly improve the performance.