End-to-end text-independent speaker verification with flexibility in utterance duration

We continue to investigate end-to-end text-independent speaker verification by incorporating the variability from different utterance durations. Our previous study [1] showed a competitive performance with a triplet loss based end-to-end text-independent speaker verification system. To normalize the duration variability, we provided fixed length inputs to the network by a simple cropping or padding operation. Those operations do not seem ideal, particularly for long duration where some amount of information is discarded, while an i-vector system typically has improved accuracy with an increase in input duration. In this study, we propose to replace the final max/average pooling layer with a Spatial Pyramid Pooling layer in the Inception-Resnet-v1 architecture, which allows us to relax the fixed-length input constraint and train the entire network with the arbitrary size of input in an end-to-end fashion. In this way, the modified network can map variable length utterances into fixed length embeddings. Experiments shows that the new end-to-end system with variable size input relatively reduces EER by 8.4% over the end-to-end system with fixed-length input, and 24.0% over the i-vector/PLDA baseline system. an end-to-end system with.