40 8 2014 8 Brief Paper ACTA AUTOMATICA SINICA Vol. 40, No. 8 Augut, 2014 i-vector 1 1 1 1, (identity vector, i-vector), T i-vector T, (American National Intitute of Standard and Technology, NIST) 2008,,,,,,,,,, i-vector, 2014, 40(8): 1836 1840 DOI 10.3724/SP.J.1004.2014.01836 Total Variability Subpace Adaptation Baed Speaker Recognition LI Zhi-Yi 1 ZHANG Wei-Qiang 1 HE Liang 1 LIU Jia 1 Abtract In text-independent peaker recognition, the identity vector (i-vector) baed modeling method ha recently been proved to be the mot popular and efficient method. It i a key problem to etimate the total variability ubpace T efficiently and accurately. In thi paper, two adaptation algorithm are propoed in order to improve the performance of the i-vector bae ytem in practical environment. Experiment on the 2008 core peaker recognition evaluation dataet of American NIST and Technology and the elf-collected peaker recognition evaluation dataet demontrate that uing the propoed adaptation algorithm to adapt to the total variability ubpace T from either the tet dataet or the developing dataet i effective for improving the performance. In addition, the combination of the two adaptation algorithm can achieve almot the bet performance uing the developing dataet rather than the tet dataet. Key word i-vector, total variability ubpace, adaptation, peaker recognition Citation Li Zhi-Yi, Zhang Wei-Qiang, He Liang, Liu Jia. Total variability ubpace adaptation baed peaker recognition. Acta Automatica Sinica, 2014, 40(8): 1836 1840 2013-11-13 2013-11-23 Manucript received November 13, 2013; accepted November 23, 2013 Recommended by Aociate Editor WU Xi-Hong (61370034, 61273268, 61005019, 90920302), (KZ201110005005), [1]., i-vector, [2 3], (American National Intitute of Standard and Technology, NIST), - (Gauian mixture model uper vector-upport vector machine, GSV-SVM) [4] (Joint factor analyi, JFA) [5 6], i-vector GSV-SVM JFA, - (Gauian mixture model-univeral background model, GMM-UBM) [7],, (1) M = m + T w (1), M, m T,,, T,,, i-vector, i-vector (Linear dicriminate analyi, LDA) (Within cla covariance normalization, WCCN). LDA i-vector, WCCN, LDA WCCN i-vector, i-vector (Coine ditance coring, CDS) SVM [8], [2] CDS i-vector, T. T i-vector, NIST, i-vector GSV-SVM, NIST,,, i-vector, Supported by National Natural Science Foundation of China (61370034, 61273268, 61005019, 90920302) and Beijing Natural Science Foundation (KZ201110005005) 1. 100084 1. Tinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tinghua Univerity, Beijing 100084
8 : i-vector 1837,,, [9], i-vector GSV-SVM i-vector T, i-vector,, : 1 i-vector T ; 2 i-vector ; 3 T, ; 4 ; 5 1 T 1.1 i-vector T, UBM,, (Expectation maximum, EM) UBM,, UBM (Maximum a poterior, MAP), x,t, UBM m N c,, F c, S c, (2) N c, = t F c, = t γ c,,t S c, = diag{ t γ c,,t(x,t m c) γ c,,t(x,t m c)(x,t m c) T } (2) m c UBM m c. t γ c,,t UBM c diag{ } F, C F C. 1.2 T, T (Expectation maximum, EM), T, T, w, (3). F F c, F C 1. N N c, F C F C. L = I + T T Σ 1 N T E[w ] = L 1 T T Σ 1 F E[w w T ] = E[w ]E[w T ] + L 1 (3) L, Σ UBM T Σ. T (4), [10] N T E[w w T ] = F E[w ] (4) UBM Σ (5) Σ = N 1 S N 1 diag{ F E[w T ]T T } (5) S S c, F C F C, N = N 6 8, T Σ 2 i-vector i-vector [2], LDA WCCN i-vector, i-vector 2.1 [11] (Linear dicriminant analyi, LDA) i-vector, LDA i-vector LDA (6), J(w) = wt S Bw w T S W w (6) S B S W (7) (8) S B = S W = (6) (w w)(w w) T (7) =1 1 n n =1 i=1 (w i w )(w i w ) T (8) w = (1/n n ) i=1 w i i-vector S, n i-vector (6) (9) 2.2 S Bw = λs W w (9) [12] (Within cla covariance normalization, WCCN) WCCN (10) W = 1 S 1 n n =1 i=1 (w i w )(w i w ) T (10)
1838 40 w = (1/n ) n i=1 w i i-vector S, n i-vector 2.3 [2], i-vector i-vector i-vector, w tar w tt, θ,, (11) core(w tar, w tt) = w tar, w tt θ (11) w tar w tt,,,,, 3 T JFA, i-vector T, T, T,,, 3.1 -, 1.2 T o,, T o,, 1 2. T, L, w E[w ] E[w w T ]; 3. 2 T, (4) ; 4. 2 3 UBM Σ, (5) ; 5. 2 ; 3.2 [13 14],,, i-vector,, 2 Fig. 2 T Diagram of total variability ubpace T combination adaptation algorithm 2, 1.2 T o T n,, : 2. T 1. T o; 2. T n; 3. T o T n T. 3.3,,, 3 Fig. 1 1 T Total variability diagram of total variability ubpace T iteration adaptation algorithm, T o,, EM 1, 1 1. T 1. T o UBM Σ, ; 3 Fig. 3 T Diagram of total variability ubpace T integration algorithm of iteration adaptation and ubpace combination adaptation
8 : i-vector 1839 3, 1.2 T o, 1 T n,, 3. T 1. T o; 2. 1, T n; 3. T o T n T. 3.4,,,,, 4, i-vector,,,,,,,, 4.1, NIST SRE 2 008, Switchboard I II 20 000, UBM, ZTnorm LDA WCCN, 12 922, 3 000, 20 000, UBM, ZTnorm LDA WCCN, 8 000, 2 000 Mel (Mel-frequency ceptral coefficient, MFCC) G.723.1 (Voice activity detection, VAD) (Ceptral mean ubtraction, CMS), 3 (Feature warping), 25 % ( 0.95)., 13, 39 MFCC UBM 1 024, i-vector T 400, 6. LDA 200. 4.2 (Equal error rate, EER) (Minimum detection cot function, MinDCF). 1 2 T T,,,,,, T,, 1 T T NIST SRE 2 008 Table 1 algorithm and the propoed iteration adaptation T algorithm 2 on NIST SRE 2 008 core dataet T 5.41 0.029 T 4.92 0.026 T 4.67 0.023 T T Table 2 algorithm and the propoed iteration adaptation T algorithm on actual application dataet T 3.00 0.014 T 2.99 0.013 T 2.00 0.011 3 T NIST SRE 2 008 Table 3 algorithm and the propoed integration algorithm of iteration adaptation and ubpace combination adaptation on NIST SRE 2008 core dataet T 5.41 0.029 T 4.01 0.021 T 3.89 0.020 1 2, 3.3 3 4,,,
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