Deep CNN based feature extractor for text-prompted speaker recognition
This work addresses speaker verification for security applications, but it is incremental as it builds on existing deep learning methods in a field where they are not yet common.
The paper tackled text-prompted speaker verification by using a deep convolutional neural network with a Max-Feature-Map activation function and multitask learning, achieving 2.85% EER on the RSR2015 dataset.
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.