%Aigaion2 BibTeX export from Idiap Publications
%Wednesday 20 November 2024 06:25:40 PM

@TECHREPORT{Dey_Idiap-RR-08-2016,
         author = {Dey, Subhadeep and Madikeri, Srikanth and Ferras, Marc and Motlicek, Petr},
       projects = {Idiap, SIIP},
          month = {4},
          title = {DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION},
           type = {Idiap-RR},
         number = {Idiap-RR-08-2016},
           year = {2016},
    institution = {Idiap},
       abstract = {The i-vector and Joint Factor Analysis (JFA) systems for text-
dependent speaker verification use sufficient statistics computed
from a speech utterance to estimate speaker models. These statis-
tics average the acoustic information over the utterance thereby
losing all the sequence information. In this paper, we study ex-
plicit content matching using Dynamic Time Warping (DTW) and
present the best achievable error rates for speaker-dependent and
speaker-independent content matching. For this purpose, a Deep
Neural Network/Hidden Markov Model Automatic Speech Recog-
nition (DNN/HMM ASR) system is used to extract content-related
posterior probabilities. This approach outperforms systems using
Gaussian mixture model posteriors by at least 50\% Equal Error Rate
(EER) on the RSR2015 in content mismatch trials. DNN posteriors
are also used in i-vector and JFA systems, obtaining EERs as low as
0.02\%.},
            pdf = {https://publications.idiap.ch/attachments/reports/2016/Dey_Idiap-RR-08-2016.pdf}
}