Towards PLDA-RBM based Speaker Recognition in Mobile Environment: Designing Stacked/Deep PLDA-RBM Systems
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1 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Toward PLDA-RBM baed Speaker Recognition in Mobile Environment: Deigning Stacked/Deep PLDA-RBM Sytem A. Nautch, H. Hao, T. Stafylaki, C. Rathgeb, C. Buch Hochchule Darmtadt, CASED, da/ec Security Reearch Group Technical Univerity of Denmark Centre de Recherche nformatique de Montréal (CRM) Shanghai,
2 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Outline 1. ntroduction 2. Propoing tacked/deep deign for PLDA-RBM 3. Experimental reult on mobile data 4. Concluion
3 ntroduction Motivation MOBO SRE 13 [Khoury+13]: limited mobile background data, i.e. tate-of-the-art i-vector & PLDA perform inufficiently Retricted Boltzman Machine (RBM) a PLDA-analogue with two-layer, undirected graphical model [Stafylaki+12] Deep Learning i more and more utilized in Speaker Recognition for robut etimation of feature pace Exploiting i-vector recontruction by deep PLDA-RBM deign, i.e.: recovering biometric information [Khoury+13] E. Khoury et al.: The 2013 Speaker Recognition Evaluation in Mobile Environment, APR CB, [Stafylaki+12] T. Stafylaki, P. Kenny, M. Senouaoui, P. Dumouchel: PLDA uing Gauian Retricted Boltzmann Machine with Application to Speaker Verification, SCA nterpeech, Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14
4 [Stafylaki+12] T. Stafylaki, P. Kenny, M. Senouaoui, P. Dumouchel: PLDA uing Gauian Retricted Boltzmann Machine with Application to Speaker Verification, SCA nterpeech, Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 ntroduction Reviiting PLDA, RBM and PLDA-RBM PLDA: decompoing i-vector x into peaker h and channel θ factor θ RBM: bipartite undirected graphical model, w/o ame-layer connection, with viible unit x and hidden unit h PLDA-RBM: decompoing peaker h peaker h x J h h j... W w i,j x x i... PLDA-RBM h peaker and channel h channel c h channel c unit h channel c+1 h channel... x,c x,c+1 x... h peaker ref h peaker prb Comparator (coine)
5 Exploiting PLDA-RBM Type and role of Energy Function Modeling the ditribution of viible and hidden layer Relevant for updating weight W Bernoulli-baed RBM Viible layer: binary variable e.g., hand-writing digit data Hidden layer: efficient claification feature Gauian-baed RBM Viible layer: real-value, continuou data Hidden layer: G-PLDA alike Gauian ub-pace, LLR coring Gauian-Gauian (GG) and Gauian-Bernoulli (GB) RBM [Stafylaki+12] T. Stafylaki, P. Kenny, M. Senouaoui, P. Dumouchel: PLDA uing Gauian Retricted Boltzmann Machine with Application to Speaker Verification, SCA nterpeech, [Yamahita+14] T. Yamahita, M. Tanaka, E. Yohida, Y. Yamauchi, H. Fujiyohi: To be Bernoulli or to be Gauian, for a Retricted Boltzmann Machine, APR EEE CPR, Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14
6 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Exploiting PLDA-RBM Deigning tacking concept: Deep PLDA-RBM (a) Stacking on channel unit ĥ peaker ĥ channel c ĥ peaker ĥ channel c Layer-N Layer-N h peaker h channel c h peaker h channel c PLDA-RBM PLDA-RBM Aumption: channel unit till comprie biometric data Recontructing biometric feature vector Selecting lat recontruction ĥ peaker Concatenating all {h peaker,..., ĥpeaker } of Layer-N
7 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Exploiting PLDA-RBM Deigning tacking concept: Deep PLDA-RBM (b) Stacking on peaker unit h peaker h channel c Layer-N h peaker PLDA-RBM Aumption: peaker unit till comprie non-biometric data Recontructing biometric feature vector: Selecting lat recontruction of Layer-N h peaker
8 Experimental et-up Experimental Reult PLDA-RBM CD1 training, tandard L2-regularization Mini-batche of 1 4 i-vector/ubject Matlab implementation (MEDAL) [Stanbury13] Conducted analye Baeline comparion of G-PLDA [GarciaEpy11] to: GG PLDA-RBM [Stafylaki+12] and GB PLDA-RBM Examining the impact of #unit Comparion of tacking concept No calibration, performance reporting: C min llr [Stanbury13] D.E. Stanbury: Matlab Environment for Deep Architecture Learning (MEDAL) v0.1, , [Online; acceed ]. [GarciaEpy11] D. Garcia-Romero, C. Y. Epy-Wilon: Analyi of i-vector Length Normalization in Speaker Recognition Sytem, SCA nterpeech, [Stafylaki+12] T. Stafylaki, P. Kenny, M. Senouaoui, P. Dumouchel: PLDA uing Gauian Retricted Boltzmann Machine with Application to Speaker Verification, SCA nterpeech, Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14
9 [Khoury+13] E. Khoury et al.: The 2013 Speaker Recognition Evaluation in Mobile Environment, APR CB, Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Experimental et-up Experimental Reult MOBO Speaker Recognition Evaluation 2013 [Khoury+13] Databae partitioning Set Female Male #Subject #Sample #Subject #Sample Background dev-et (ref) dev-et (prb) eval-et (ref) eval-et (prb) Challenge: limited background data & mobile data, i.e.: LDA without ignificant benefit, thu no LDA MOBO SRE 13 primary metric: HTER
10 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Experimental Reult Baeline performance and #unit Sytem Female Male EER FMR100 C min llr EER FMR100 C min llr G-PLDA PLDA-RBM ingle layer GG GB Examining #unit impact on GB PLDA-RBM C llr min ,000 Number of hidden peaker and hidden channel unit Female Male
11 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Experimental Reult Comparion of tacking concept Stacking concept comparion w.r.t. #layer #layer Female Male (a) channel (b) peaker (a) channel (b) peaker Feature compoition on (a) tacking channel unit #layer Female Male ingle fued ingle fued
12 Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14 Experimental Reult Comparion to bet ytem of MOBO SRE 13 having 1 ub-ytem Sytem Female Male HTER Cllr min HTER Cllr min MOBO-female (GMM UBM) 11.6 n/a 9.1 n/a MOBO-male (GMM UBM) 12.8 n/a 8.9 n/a G-PLDA (gender-pooled) layer GB PLDA-RBM layer GB PLDA-RBM (channel-tacked)
13 Experimental Reult Concluion PLDA-RBM i applicable for (limited) mobile data Benefit from GB aumption GB PLDA-RBM outperform conventional G-PLDA Recovering biometric information by exploiting deeper layer Propoing tacking on channel unit concept Biometric information decreae in higher layer Accumulation of biometric data by {hpeaker,..., h peaker } High computational effort, providing more reliable evidence Perpective Examining large-cale NST SRE databae Robut RBM training e.g., drop-out, fine-tuning Deep layer deign e.g., GB-GB v. GB-BB v. GB-BG Thi work ha been funded by the Center for Advanced Security Reearch Darmtadt (CASED), and the Hee government (project no. 467/15-09, BioMobile). Nautch, Hao, Stafylaki, Rathgeb, Buch PLDA-RBM mobile data / Shanghai, /14
14 Andrea Nautch Doctoral Reearcher Reearch Area: Secure Service CASED Mornewegtr Darmtadt/Germany Telefon Fax
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