IPSJ SIG Techncal Report 歌声データベース 歌声の波形 スペクトル抽出 基本周波数抽出 HMM メルケプストラム ラベル HMM の学習 対数基本周波数 c 学習部 コンテキスト依存モデル c ( 合成部 楽譜 ラベル変換 ラベル... メルケプストラム パラメータ生成 ML
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1 IPSJ SIG Techncal Report HMM HMM HMM HMM 60 Vbrato Modelng for HMM-based Sngng Voce Synthess Tomohko Yamada, Satoru Muto, Yoshhko ankaku, Shnj Sako and Kech Tokuda HMM-based sngng voce synthess can automatcally learn snger s features from sngng voce waveform and muscal scores and synthesze sngng voce whch the features are reflected n wth any melody. The features n the sgner s tone and pronuncaton or ptch are modeled as a sequence of spectrum or fundamental frequency(f0 by HMM. In ths report, we assume that vbrato s a perodc fluctuaton of ptch and t s modeled by snusod. That parameters are modeled by HMM wth spectrum and F0 smultaneously. In the experments of subjectve assessment, we confrmed that smooth and natural sngng voce s syntheszed.. Hdden Markov Model; HMM HMM 2,3 HMM 4 HMM HMM HMM 2 HMM Department of Computer Scence and Engneerng, agoya Insttute of Technology c 2009 Informaton Processng Socety of Japan
2 IPSJ SIG Techncal Report 歌声データベース 歌声の波形 スペクトル抽出 基本周波数抽出 HMM メルケプストラム ラベル HMM の学習 対数基本周波数 c 学習部 コンテキスト依存モデル c ( 合成部 楽譜 ラベル変換 ラベル... メルケプストラム パラメータ生成 MLSA フィルタ 励振 対数基本周波数 パルス信号 合成された歌声 HMM Fg. HMM-based sngng voce system 2 c 2 ( p p 3 ( 2 p 4 ( 2 Fg. 2 Vectoral structure of tranng data 4 Y? Y Y? a? 3 Fg. 3 Constructng decson trees by clusterng Y? 2. HMM HMM HMM 2 HMM c p 0 HMM Mult-Space Probablty Dstrbuton HMM; MSD-HMM 5 2. HMM 4 a u ( yes no ( 2 2 c 2009 Informaton Processng Socety of Japan
3 IPSJ SIG Techncal Report : 4 Fg. 4 Example of vbrato n F0 ( 3 yes no ( Mnmum Descrpton Length; MDL MLSA 9 MuscXML HMM 3. t = 0,,..., M [t (0, t ( ] v( cent ( v(m a (t, m f (t, = m a (t sn 2π m f (t (t t (0 ( f r f r m a (t m f (t t cent Hz m a(t = 0 m f (t = 0 m a(t m f (t 3.2 [t (0, t ( ] 30 50cent 5 8Hz lf 0 (t 5 c = log 2/200 cent t 0, t, m a(t m f (t m a m f HMM 7 o t 3 o (spec t, o (f 0 t, o (vb t b s(o t s o t 3 c 2009 Informaton Processng Socety of Japan
4 IPSJ SIG Techncal Report mf(t0 mf(t2 mf(t4 c 2c ma(t0 2c ma(t2 2c ma(t4 ( c 2 c log f0 2c ma(t 2c ma(t3 2 ( p p 3 ( 2 p m a m f 4 ( 5 ( mf(t mf(t3 7 Fg. 7 Structure of tranng data combned vbrato parameters 0 [cent] max ma(t mn Fg. 6 0 t (0 t 0 t t 2 t 3 t 4 t ( 5 Fg. 5 Analyzng vbrato parameters t (0 t 0 t t 2 t 3 t 4 t ( 6 ( Lnear nterpolaton of vbrato parameters(ampltude parameter b s(o t = p γspec s (o (spec t p γ f 0 s (o (f 0 t p γ vb s (o (vb t (2 γ spec, γ f0, γ vb 3 2 ( γ vb b s (o t (2 2 γ vb 0 b s (o t 3.5 HMM lf 0 (t { lf 0(t lf 0(t + c v(m a(t, m f (t, (t (0 t t ( = lf 0(t (otherwse lf 0(t v( m a (t m f (t, v( 3.2 (3 4 c 2009 Informaton Processng Socety of Japan
5 IPSJ SIG Techncal Report Table Sngng voce database kHz 6bt short 2 ( Table 2 Expermental condton for Mel-cepstral analyss FFT 6kHz 024pont 5ms 24 STRAIGHT 3 log f0 log f (a ( [sec] [sec] 3 ( Table 3 Expermental condton for F0 analyss 6kHz 5ms TEMPO 4 MLSA 4. HMM 3.3 MuscXML 2 3 HMM HSMM 2 HSMM 5 left-to-rght 8 a b Fg (b ( 8 Generated F0 contour for a Japanese song ZOUSA 3 2 A B- B MOS 5 c 2009 Informaton Processng Socety of Japan
6 IPSJ SIG Techncal Report % Fg MOS A B- B-2 9 Result of MOS test for syntheszed sngng voces 8 HMM 9 5. HMM Vol.45, o.3, pp ( HMM (D-II Vol.J79-D-II, o.2, pp ( HMM (D-II Vol.J83-D-II, o., pp ( Sano, K., Zen, H., ankaku, Y., Lee, A. and Tokuda, K.: An HMM-based Sngng Voce Synthess System, Proc. of Interspeech 2006, Vol., pp. 4 ( HMM (D-II Vol.J83-D-II, o.7, pp ( Odell, J.J.: The use of context n large vocabulary speech recognton, PhD dssertaton, Cambrdge Unversty ( Shnoda, K. and Watanabe, T.: Acoustc modelng based on the MDL crteron for speech recognton, Proc. EuroSpeech, pp ( Tokuda, K., Kobayash, T. and Ima, S.: Speech parameter generaton from HMM usng dynamc features, Proc. ICASSP-95, pp ( MLSA A Vol.J66-A, o.2, pp ( Recordare: MuscXML 2.0 Tutoral. Vol.48, o., pp ( Zen, H., Masuko, T., Tokuda, K., Kobayash, T. and Ktamura, T.: A Hdden Sem-Markov Model-Based Speech Synthess System, IEICE Trans. Inf. & Syst., Vol.90D, o.5, pp ( Kawahara, H., Masuda-Katsuse, I. and Chevegne, A.: Speech representatons usng a ptch-adaptve tme-frequency smoothng and an nstantaneous-frequencybased f0 extracton: possble role of a repettve structure n sound, Speech Communcaton, Vol.27, pp ( Zolfaghar, P. Chevegne, A. Patterson, R.D. Vol.SP99, o.40 ( c 2009 Informaton Processng Socety of Japan
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