Title: Deep CNN based feature extractor for text-prompted speaker recognition,
Abstract: Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level feature extractor for all states and use cosine similarity
metric for scoring. The key feature of our network is the Max-Feature-Map
activation function, which acts as an embedded feature selector. By using
multitask learning scheme to train the high-level feature extractor we were
able to surpass the classic baseline systems in terms of quality and achieved
impressive results for such a novice approach, getting 2.85% EER on the RSR2015
evaluation set. Fusion of the proposed and the baseline systems improves this
result.