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11 , 1 2, U2 3 U 1 U b 1 (o t ) b 2 (o t ) b 3 (o t ), 3 b (o t )
12 MULTI-SPEAKER SPEECH DATABASE Training Speech Analysis Mel-Cepstrum, logf0 /context1/ /context2/... Context Dependent HMMs (Average Voice Model)
13
14 Average Voice Model Speaker Adaptation Adapted Model ADAPTATION DATA
15 Adapted Model Sentence HMM TEXT c 1 c 2 p 1 p 2 F0 PARAMETER GENERATION Mel-Cepstrum Excitation MLSA Filter SYNTHESIZED SPEECH
16
17 F0 no yes no yes no yes no yes no yes no yes no yes
18 MDL Yes No Yes No Clustering Context Dependent HMMs
19 y y n n
20 a-b-a a-a-b b-b-a y n b-a-a b-a-b a a b a a-b-a a-a-b b-b-a b-a-a b-a-a
21 y a-b-a a-a-b b-b-a a n y b-a-a b-a-b a n a-b-a a-a-b b-b-a a b-a-a b-a-b
22
23 y y n n Average Voice Model
24 HMM ATRB 16kHz 5ms 25ms 024 left-to-right
25 FKN FKS FYM MHO MHT MYI A B C D E F A,B B,C C,D D,E E,F F,G A~C B~D C~E D~F E~G F~H A~D B~E C~F D~G E~H F~I A~E B~F C~G D~H E~I A,F~I A~F B~G C~H D~I A,E~I A,B,F~I AI
26 50 F0 (A) (B) (A) (B) ( 8%) 505 (50%) 14 ( 3%) 197 (19%) (0%) 0 (0%) 0 (0%) 0 (0%) (A) (B)
27 Frequency [Hz] Time [s]
28 13 538
29 sentences per speaker score[%]
30
31 話者適応学習 (SATアルゴリズム) 話者適応に適した 平均声モデルを作成するための 話者正規化学習アルゴリズム
32 /a/ Average Voice Speaker 1 Speaker 2 logf0
33 /a/ Average Voice Speaker 1 Speaker 2 logf0 Speaker Adaptive Training [T. Anastasakos et al., 96]
34 [C.J. Leggetter et al., 96] m m Acoustic Space Dimension 2 Average Voice 2 1 ˆ 2 W ˆ 1 Speaker A Acoustic Space Dimension 1
35 Speaker 1 Speaker 2 Average Voice Model W i Speaker 3
36
37 Context Dependent Model (SI) Tied Context Dependent Model (SI) Context Dependent Models (SD) Tied Context Dependent Model (SI) Average Voice Model Average Voice Model SI SD
38 Average Voice Model (NONESATSTCSTC+SAT) Speaker Adapted Model MMY FTK Speaker Dependent Model MMY FTK
39
40 NONE 2.65 SAT 2.79 STC 3.01 STC+SAT 3.52 NONE 2.33 SAT 2.66 STC 2.95 STC+SAT 3.43 SD 3.84 MMY SD FTK Score SD
41 NONE SATSAT STCSTC STCSATSTC+SAT SD
42 HSMMに基づく 話者適応アルゴリズム 隠れセミマルコフモデルに基づく スペクトル F０ 音韻継続長の 同時適応アルゴリズム
43 , 1 2, U2 3 U 1 U b 1 (o t ) b 2 (o t ) b 3 (o t ), 3 b (o t )
44 [J.D. Ferguson 80, S.E. Levinson 86] p(d 1 ) p(d 2 ) p(d 3 ) p (d i ) b i(o t ) b 1 (o t ) b 2 (o t ) b 3 (o t )
45 HSMM d time
46
47 [J. Yamagishi et. al. 04] W X Acoustic Space Dimension 2 Average Voice Model Speaker A Acoustic Space Dimension 1
48 [J. Yamagishi et. al. 04]
49 Threshold Target Speaker s Model Average Voice Model
50 [J. Yamagishi et. al. 05] p(d 1 ) p(d 2 ) p(d 3 ) p (d i ) b i(o t ) b 1 (o t ) b 2 (o t ) b 3 (o t )
51 [J. Yamagishi et. al. 05] Average Voice Model Speaker 1 Speaker 2 X 1 X2 W 1 W 2 X 3 W 3 Speaker 3 W i X i
52 Δ, Δ
53 9.0 MHO MYI Average mora/sec MHT MSH MMY FKS FYM FKN FTY MTK FTK Average logarithm of F0
54 73 Average log-likelihood per frame Both Output Duration None Number of Sentences
55 9.0 Average mora/sec Average Voice (Male Speakers) MTK(MLLR) Average Voice (Female Speakers) FTK MTK FTK(MLLR) Average logarithm of F0
56 RMSE of logf0 [cent] Average Voice SD MLLR Number of Sentences
57 8 SD Mel-cepstrum Distance [db] Average Voice MLLR Number of Sentences
58 11 RMSE of Vowel Duration [frame] Average Voice SD MLLR Number of Sentences
59
60 Spectrum F0 Duration SD SD SD Average Voice Adaptation
61
62 Spectrum Spectrum +F0 Spectrum +F0 +Duration Score (%)
63 Spectrum F0 Duration SD SD SD Average Voice Adaptation
64
65
66
67
68 1. J. Yamagishi and T. Kobayashi, Simultaneous Speaker Adaptation Algorithm of Spectrum, Fundamental Frequency and Duration for HMM-based Speech Synthesis, IEICE Trans. Information and Systems. (in preparation) 2. J. Yamagishi, Y. Nakano, K. Ogata, J. Isogai, and T. Kobayashi, A Unified Speech Synthesis Method Using HSMM-Based Speaker Adaptation and MAP Modification, IEICE Trans. Information and Systems. (in preparation) 3. J. Yamagishi, K. Onishi, T. Masuko, and T. Kobayashi, Acoustic Modeling of Speaking Styles and Emotional Expressions in HMM-based Speech Synthesis, IEICE Trans. Information and Systems, E88-D, vol.3, pp , March J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, A Training Method of Average Voice Model for HMM-based Speech Synthesis, IEICE Trans. Fundamentals, E86-A, no.8, pp , Aug J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, A Context Clustering Technique for Average Voice Models, IEICE Trans. Information and Systems, E86-D, no.3, pp , March 2003
69 1. J. Yamagishi, K. Ogata, Y. Nakano, J. Isogai, and T. Kobayashi, HSMM-based Model Adaptation Algorithms for Average-Voice-based Speech Synthesis, Proc. ICASSP 2006, May 2006 (submit). 2. J. Yamagishi, and T. Kobayashi, Adaptive Training for Hidden Semi-Markov Model, Proc. ICASSP 2005, vol.i, pp , March J. Yamagishi, T. Masuko, and T. Kobayashi, MLLR Adaptation for Hidden Semi-Markov Model Based Speech Synthesis, Proc. ICSLP 2004, vo.ii, pp , October J. Yamagishi, M. Tachibana, T. Masuko, and T. Kobayashi, Speaking Style Adaptation Using Context Clustering Decision Tree for HMM-based Speech Synthesis, Proc. ICASSP 2004, vol.i, pp.5 8, May J. Yamagishi, T. Masuko, and T. Kobayashi, HMM-based Expressive Speech Synthesis Towards TTS with Arbitrary Speaking Styles and Emotions, Special Workshop in Maui (SWIM), January J. Yamagishi, K. Onishi, T. Masuko, and T. Kobayashi, Modeling of Various Speaking Styles and Emotions for HMM-based Speech Synthesis, Proc. EUROSPEECH 2003, vol.iii, pp , September J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, A Training Method for Average Voice Model Based on Shared Decision Tree Context Clustering and Speaker Adaptive Training, Proc. ICASSP 2003, vol.i, pp , April J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, A Context Clustering Technique for Average Voice Model in HMM-based Speech Synthesis, Proc. ICSLP 2002, vol.1, pp , September 2002.
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