Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic Time Warping

no code implementations • 26 Jun 2018 • Mohamed Adel, Mohamed Afify, Akram Gaballah

The d-vectors, generated from a feed forward deep neural network trained to distinguish between speakers, are used as features to perform alignment and hence calculate the overall distance between the enrolment and test utterances. We present results on the NIST 2008 data set for speaker verification where the proposed method outperforms the conventional i-vector baseline with PLDA scores and outperforms d-vector approach with local distances based on cosine and PLDA scores.

Dynamic Time Warping Text-Independent Speaker Verification