DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION
| Type of publication: | Idiap-RR |
| Citation: | Dey_Idiap-RR-08-2016 |
| Number: | Idiap-RR-08-2016 |
| Year: | 2016 |
| Month: | 4 |
| 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%. |
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| Projects: |
Idiap SIIP |
| Authors: | |
| Crossref by |
Dey_ICASSP_2016 |
| Added by: | [ADM] |
| Total mark: | 0 |
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