. i-vector, Total Variability Subspace Adaptation Based Speaker Recognition. Brief Paper ACTA AUTOMATICA SINICA Vol. 40, No. 8 August, 2014.

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download ". i-vector, Total Variability Subspace Adaptation Based Speaker Recognition. Brief Paper ACTA AUTOMATICA SINICA Vol. 40, No. 8 August, 2014."

Πρίσκιλλα Καψής
5 χρόνια πριν
Προβολές:

1 Brief Paper ACTA AUTOMATICA SINICA Vol. 40, No. 8 Augut, 2014 i-vector , (identity vector, i-vector), T i-vector T, (American National Intitute of Standard and Technology, NIST) 2008,,,,,,,,,, i-vector, 2014, 40(8): DOI /SP.J 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): Manucript received November 13, 2013; accepted November 23, 2013 Recommended by Aociate Editor WU Xi-Hong ( , , , ), (KZ ), [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 ( , , , ) and Beijing Natural Science Foundation (KZ ) Tinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tinghua Univerity, Beijing

2 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)

3 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) ; 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

4 8 : i-vector , 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 , UBM, ZTnorm LDA WCCN, , 3 000, , UBM, ZTnorm LDA WCCN, 8 000, Mel (Mel-frequency ceptral coefficient, MFCC) G (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 (Equal error rate, EER) (Minimum detection cot function, MinDCF). 1 2 T T,,,,,, T,, 1 T T NIST SRE Table 1 algorithm and the propoed iteration adaptation T algorithm 2 on NIST SRE core dataet T T T T T Table 2 algorithm and the propoed iteration adaptation T algorithm on actual application dataet T T T T NIST SRE Table 3 algorithm and the propoed integration algorithm of iteration adaptation and ubpace combination adaptation on NIST SRE 2008 core dataet T T T , ,,,

5 Table 4 T algorithm and the propoed integration algorithm of iteration adaptation and ubpace combination adaptation on actual application dataet T T T ,,,,, i-vector 5 i-vector, T,, i-vector T,,. NIST SRE 2008,,,,, i-vector Reference 1 Kinnunen T, Li H Z. An overview of text-independent peaker recognition: from feature to upervector. Speech Communication, 2010, 52(1): Dehak N, Kenny P, Ouellet P, Dumouchel P. Front-end factor analyi for peaker verification. IEEE Tranaction on Audio, Speech and Language Proceing, 2011, 19(4): Li Zhi-Yi, He Liang, Zhang Wei-Qiang, Liu Jia. Speaker recognition baed on dicriminant i-vector local ditance preerving projection. Journal of Tinghua Univerity (Science and Technology), 2012, 52(5): (,,, i-vector ( ), 2012, 52(5): ) 4 Campbell W M, Campbell J P, Reynold D A, Singer E, Torre-Carraquillo P A. Support vector machine for peaker and language recognition. Computer Speech and Language, 2006, 20(2 3): Kenny P, Boulianne G, Ouellet P, Dumouchel P. Speaker and eion variability in GMM-baed peaker verification. IEEE Tranaction on Audio, Speech and Language Proceing, 2007, 15(4): Kenny P, Boulianne G, Ouellet P, Dumouchel P. Joint factor analyi veru eigenchannel in peaker recognition. IEEE Tranaction on Audio, Speech and Language Proceing, 2007, 15(4): Reynold D A, Quatieri T F, Dunn R B. Speaker verification uing adapted Gauian mixture model. Digital Signal Proceing, 2000, 10(1 3): Corte C, Vapnik V. Support vector network. Machine Learning, 1995, 20(3): Zhang Wen-Lin, Zhang Wei-Qiang, Liu Jia, Li Bi-Cheng, Qu Dan. A new ubpace baed peaker adaptation method. Acta Automatica Sinica, 2011, 37(12): (,,,,, 2011, 37(12): ) 10 Kenny P, Boulianne G, Dumouchel P. Eigenvoice modeling with pare training data. IEEE Tranaction on Audio, Speech, and Language Proceing, 2005, 13(3): Bihop C M. Pattern Recognition and Machine Learning. Berlin: Springer, Hatch A O, Kajarekar S, Stolcke A. Within-cla covariance normalization for SVM-baed peaker recognition. In: Proceeding of the International Conference on Spoken Language Proceing. Pittburgh, PA, He Liang, Shi Yong-Zhe, Liu Jia. Eigenchannel pace combination method of joint factor analyi Acta Automatica Sinica, 2011, 37(7): (,,, 2011, 37(7): ) 14 Guo Wu, Li Yi-Jie, Dai Li-Rong, Wang Ren-Hua. Factor analyi and pace aembling in peaker recognition. Acta Automatica Sinica, 2009, 35(9): (,,,, 2009, 35(9): ) lizhiyi06@mail.tinghua.edu.cn (LI Zhi-Yi Ph. D. candidate in the Department of Electronic Engineering, Tinghua Univerity. Hi reearch interet cover peaker recognition and language recognition. Correponding author of thi paper.) wqzhang@tinghua.edu.cn (ZHANG Wei-Qiang Aitant profeor in the Department of Electronic Engineering, Tinghua Univerity. Hi reearch interet cover peaker recognition and language recognition.) heliang@tinghua.edu.cn (HE Liang Aitant profeor in the Department of Electronic Engineering, Tinghua Univerity. Hi reearch interet cover peaker recognition and language recognition.). liuj@tinghua.edu.cn (LIU Jia Profeor in the Department of Electronic Engineering, Tinghua Univerity. Hi reearch interet cover peech recognition and ignal proceing.)

Σχετικά έγγραφα

A Robust Bootstrapping Algorithm of Speaker Models for On-Line Unsupervised Speaker Indexing

A Robust Bootstrapping Algorithm of Speaker Models for On-Line Unsupervised Speaker Indexing ISSN 000-9825, CODEN RUXUEW E-ail: jo@icaaccn Journal of Software, Vol8, No3, March 2007, pp608 66 http://wwwjoorgcn DOI: 0360/jo80608 Tel/Fax: +86-0-62562563 2007 by Journal of Software All right reerved