Deep CNN Based Feature Extractor for Text-Prompted Speaker Recognition

Sergey Novoselov; Oleg Kudashev; Vadim Shchemelinin; Ivan Kremnev; Galina Lavrentyeva

ICASSP 2018accepted0 citations

Deep CNN Based Feature Extractor for Text-Prompted Speaker Recognition

Sergey Novoselov, Oleg Kudashev, Vadim Shchemelinin, Ivan Kremnev, Galina Lavrentyeva

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.

BibTeX

@inproceedings{icassp2018_deepcnnbasedfeat,
  title = {Deep CNN Based Feature Extractor for Text-Prompted Speaker Recognition},
  author = {Sergey Novoselov and Oleg Kudashev and Vadim Shchemelinin and Ivan Kremnev and Galina Lavrentyeva},
  booktitle = {ICASSP 2018},
  year = {2018}
}