%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Dey_ICASSP_2016,
         author = {Dey, Subhadeep and Madikeri, Srikanth and Ferras, Marc and Motlicek, Petr},
       projects = {Idiap, SIIP},
          month = mar,
          title = {DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION},
      booktitle = {Proceedings of 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)},
           year = {2016},
          pages = {5050-5054},
      publisher = {IEEE},
       location = {Shanghai},
       crossref = {Dey_Idiap-RR-08-2016},
       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/papers/2016/Dey_ICASSP_2016.pdf}
}



crossreferenced publications: 
@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}
}