Voice Conversion based on Non-negative Matrix Factorization with Segment Features in Noisy Environments

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "Voice Conversion based on Non-negative Matrix Factorization with Segment Features in Noisy Environments"

Σίλας Δασκαλοπούλου
7 χρόνια πριν
Προβολές:

1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. NMF E-ail: {fujii,aihara}@e.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp NMF NMF NMF Voice Conversion based on Non-negative Matrix Factorization with Segent Features in Noisy Environents Takao FUJII, Ryo AIHARA, Tetsuya TAKIGUCHI, and Yasuo ARIKI Organization of Advanced Science and Technology, Kobe University Rokkodai-cho 1 1, Nada-ku, Kobe-shi, Japan Graduate School of Syste Inforatics, Kobe University Rokkodai-cho 1 1, Nada-ku, Kobe-shi, Japan E-ail: {fujii,aihara}@e.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Abstract This paper presents a voice conversion based on NMF for noisy environents. We prepared parallel exeplars that consist of the source and target exeplars, which have the sae texts uttered by the source and target speakers. The input source signal is decoposed into the source exeplars, noise exeplars obtained fro the input signal, and their weights. Then, the converted signal is obtained by calculating the linear cobination of the target exeplars and the weights which are calculated using the source exeplars. In the proposed ethod, segent features are used for the voice conversion technique based on NMF in order to iprove the accuracy of the weight estiation. The effectiveness of this ethod was confired by coparing its effectiveness with that of a conventional ethod. Key words NMF, Voice Conversion, Speaker Conversion, Noise Reduction, 1. [1], [2] [3] Gaussian Mixture Model (GMM) [4] [5] [6] [7] GMM Global Variance Helander [8] Partial Least Squares (PLS) GMM [9] Eigen-Voice GMM (EV-GMM) [10] [11] This article is a technical report without peer review, and its polished and/or extended version ay be published elsewhere. Copyright 2013 by IEICE

2 Sparse Coding Non-negative Matrix Factorization ( NMF) [12] [13] [14] Sparse Coding Geeke [15] Hidden Markov Model (HMM) NMF NMF 2. GMM 2. 1 GMM t x t y t GMM λ α μ Σ λ = {α,μ, Σ =1, 2,..., M} (1) α M α =1,α > = 0 (2) =1 N(x t ; μ, Σ ) = 1 (2π)D Σ exp[ 1 2 (xt μ)t Σ 1 (x t μ )] 2. 2 x =[x 0,x 1,..., x T ] T y =[y 0,y 1,..., y T ] T [16] (3) F (x) =E[y x] M = h (x)e [y x] (4) =1 E [y x] =[μ (y) h (x) = +Σ (yx) α N(x; μ (x), Σ (xx) M i=1 (Σ (xx) ) 1 (x μ (x) )] (5) ) αin(x; μ(x) i, Σ (xx) i ) μ (x),μ (y) Σ (xx) Σ (yx) 2. 3 z t = [x T t,yt T ] T p(z) = M =1 (6) α N(z; μ (z), Σ (z) ) (7) α,μ (x),μ (y), Σ (xx), Σ (yx) GMM Σ (z) μ (Z) [ ] (xx) Σ Σ (z) Σ (xy) = (8) μ (z) = Σ (yx) [ μ (x) μ (y) ] Σ (yy) EM Σ (xx), Σ (yx) NMF NMF [17] (9) x l J j=1 ajhj,l =Ahl (10) x l l a j j h j,l a j

3 1 Fig. 1 Activity atrix of the source signal 2 Fig. 2 Activity atrix of the target signal 3 NMF Fig. 3 Basic approach of voice conversion based on NMF A=[a 1...a J ] h l =[h 1,l...h J,l ] T (DP) 1 2 NMF STRAIGHT [18] (STRAIGHT ) 3 NMF 3. 2 STRAIGHT [18] (STFT) STRAIGHT 4 STFT STRAIGHT STRAIGHT DP STFT STRAIGHT STFT STRAIGHT 3. 3 NMF l

4 λ T =[λ 1...λ J...λ J+K ] [λ 1...λ J ] 0.2 [λ J+1...λ J+K ] H H s Fig. 4 x l =x s l +x n l 4 Construction of source and target dictionaries J j=1 as jh s j,l + K [ ] h s =[A s A n l ] s.t. h n l k=1 an k h n k,l h s l, h n l > = 0 =Ah l s.t. h l > = 0 (11) x s l x n l. A s, A n, h s l, h n l l (11) - [ ] H s X [A s A n ] s.t. H s, H n > = 0 H n =AH s.t. H > = 0. (12) X A s A n H NMF [15] M = 1 (D D) X X X./M A A./(1 (D D) A) (13) 1 1 NMF H H d(x, AH) + (λ1 (1 L) ). H 1 s.t. H > = 0. (14) H n+1 =H n. (A T (X./(AH)))./(1 ((J+K) L) + λ1 (1 L) ). (15) (14) H X AH Kullback-Leibler divergence H L1 A t A t./(1 (D D) A t ) (16) H s (13) NMF ˆX t =(A t H s ). M (17) STRAIGHT NMF STRAIGHT STRAIGHT STRAIGHT 3. 5 NMF NMF NMF GMM NMF ATR 8kHz ,033 GMM

5 5 50 Fig. 5 Cepstru distortion for each ethod in the case of using 50 words Fig. 7 7 GMM Converted spectru envelope of source speaker based on GMM Fig. 8 8 NMF Converted spectru envelope of source speaker with NMF 6 25 Fig. 6 Cepstru distortion for each ethod in the case of using 25 sentences STRAIGHT 40 GMM CENSREC-1-C SNR 10dB STRAIGHT (seg(2)) 1 (seg(3)) GMM NMF 9 Fig. 9 Converted spectru envelope of source speaker with proposed ethod 10 Fig. 10 Spectru envelope of source speaker 11 Fig. 11 Spectru envelope of target speaker

6 5 6 NMF GMM 5. NMF NMF NMF [1] Y. Iwai, T. Toda, H. Saruwatari, and K. Shikano, GMMbased Voice Conversion Applied to Eotional Speech Synthesis, IEEE Trans. Seech and Audio Proc., Vol. 7, pp , [2] C. Veaux and X. Robet, Intonation conversion fro neutral to expressive speech, in Proc. INTERSPEECH, pp , [3] K. Nakaura, T. Toda, H. Saruwatari, and K. Shikano, Speaking-aid systes using GMM-based voice conversion for electrolaryngeal speech, Speech Counication, Vol. 54, No. 1, pp , [4] Y. Stylianou, O. Cappe, and E. Moulines, Continuous probabilistic transfor for voice conversion, IEEE Trans. Speech and Audio Processing, Vol. 6, No. 2, pp , [5] M. Abe, S. Nakaura, K. Shikano, and H. Kuwabara, Vice conversion through vector quantization, in Proc. ICASSP, pp , [6] H. Valbret, E. Moulines and J. P. Tubach, Voice transforation using PSOLA technique, Speech Counication, Vol. 11, No. 2-3, pp , [7] T. Toda, A. Black, and K. Tokuda, Voice conversion based on axiu likelihood estiation of spectral paraeter trajectory, IEEE Trans. Audio, Speech, Lang. Process., Vol. 15, No. 8, pp , [8] E. Helander, T. Virtanen, J. Nurinen, and M. Gabbouj, Voice conversion using partial least squares regression, IEEE Trans. Audo, Speech, Lang. Process., Vol. 18, No. 5, pp , [9] C. H. Lee and C. H. Wu, Map-based adaptation for speech conversion using adaptation data selection and non-parallel training, in Proc. INTERSPEECH, pp , [10] T. Toda, Y. Ohtani, and K. Shikano, Eigenvoice conversion based on Gaussian ixture odel, in Proc. INTER- SPEECH, pp , [11] D. Saito, K. Yaaoto, N. Mineatsu, and K. Hirose, One-to-any voice conversion based on tensor representation of speaker space, in Proc. INTERSPEECH, pp , [12] D. D. Lee and H. S. Seung, Algoriths for non-negative atrix factorization, in Proc. Neural Inforation Processing Syste, pp , [13] T. Virtanen, Monaural sound source separation by nonnegative atrix factorization with teporal continuity and sparseness criteria, IEEE Trans. Audio, Speech, Lang. Process., Vol. 15, No. 3, pp , [14] M. N. Schidt and R. K. Olsson, Single-channel speech separation using sparse non-negative atrix factorization, in Proc. INTERSPEECH, pp , [15] J. F. Geeke, T. Viratnen, and A. Huralainen, Exeplar-Based Sparse Representations for Noise Robust Autoatic Speech Recognition, IEEE Trans. Audio, Speech, Lang. Process., Vol. 19, Issue 7, pp , [16] Y. Stylianou, O. Cappe and E. Moilines, Statistical ethods for voice quality transforation, in Proc. European speech Association, [17] Ryoichi Takashia, Tetsuya Takiguchi, and Yasuo Ariki, Exeplar-Based Voice Conversion in Noisy Environent, IEEE Workshop on Spoken Language Technology (SLT2012), pp , [18] H. Kawahara, I. Masuda-Katsuse, and A.de Cheveigne, Restructuring speech representations using a pitchadaptive tie-frequency soothing and an instantaneousfrequency-based F0 extraction: Possible role of a repetitive structure in sounds, Speech Counication, Vol.27, pp ,

Σχετικά έγγραφα

1,a) 1,b) 2 3 Sakriani Sakti 1 Graham Neubig 1 1. A Study on HMM-Based Speech Synthesis Using Rich Context Models

1,a) 1,b) 2 3 Sakriani Sakti 1 Graham Neubig 1 1. A Study on HMM-Based Speech Synthesis Using Rich Context Models HMM 1,a 1,b 3 Sakriani Sakti 1 Graham Neubig 1 1 Hidden Markov Model HMM HMM HMM HMM HMM A Study on HMM-Based Speech Synthesis Using Rich Context Models Shinnosuke Takamichi 1,a Toda Tomoki 1,b Shiga Yoshinori