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DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION
Type of publication: Conference paper
Citation: Dey_ICASSP_2016
Publication status: Published
Booktitle: Proceedings of 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)
Year: 2016
Month: March
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%.
Keywords:
Projects Idiap
SIIP
Authors Dey, Subhadeep
Madikeri, Srikanth
Ferras, Marc
Motlicek, Petr
Added by: [UNK]
Total mark: 0
Attachments
  • Dey_ICASSP_2016.pdf
Notes