Deep Speaker: an End-to-End Neural Speaker Embedding System
Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training.
['system', 'EER[%]', 'ACC[%]']
We do not experiment with the i-vector baseline here, as it’s too time consuming and computationally expensive. It is clear that using tens of millions of samples results in a performance boost. Compared to using only around 1/5th the data, the using the full dataset reduces the identification error and EER by 17.94% and 21.65% for ResCNN and 15.16% and 13.88% for GRU.