Technical Program

Paper Detail

Presentation #1
Session:Speaker Recognition/Verification
Location:Kallirhoe Hall
Session Time:Thursday, December 20, 10:00 - 12:00
Presentation Time:Thursday, December 20, 10:00 - 12:00
Presentation: Poster
Topic: Speaker/language recognition:
Paper Title: Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic Time Warping
Authors: Mohamed Adel, Mohamed Afify, Microsoft Advanced Technology Lab, Cairo, Egypt; Akram Gaballah, Microsoft Corporation, United States; Magda Fayek, Cairo University, Egypt
Abstract: In this paper we present a new method for text-independent speaker verification that combines segmental dynamic time warping (SDTW) and the d-vector approach. 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. Also score combination with the i-vector/PLDA baseline leads to significant gains over both methods.