Deep Speaker: an End-to-End Neural Speaker Embedding System
Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported.
['system', 'EER[%]', 'ACC[%]']
First, we compare the DNN-based i-vector system and Deep Speaker on the UIDs dataset, with the Train50k partition as the training set and Eva200 partition as the test set. Deep Speaker models are trained with different neural network architectures (GRU or ResCNN), and different strategies (softmax, triplet loss, or softmax + triplet loss, which is softmax pretraining followed by triplet loss fine-tuning). All Deep Speaker models achieve notable improvements over the baseline, roughly 50 - 80% relative reduction on EER, and 60 - 70% relative improvement on ACC.