|
|
|
- まいえ すえがら
- 6 years ago
- Views:
Transcription
1
2
3
4
5
6
7
8
9
10
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.
7) 8) 9),10) 11) 18) 11),16) 18) 19) 20) Vocaloid 6) Vocaloid 1 VocaListener1 2 VocaListener1 3 VocaListener VocaListener1 VocaListener1 Voca
VocaListener2: 1 1 VocaListener2 VocaListener VocaListener2 VocaListener2 VocaListener VocaListener2 VocaListener2: A Singing Synthesis System Mimicking Voice Timbre Changes in Addition to Pitch and Dynamics
log F0 意識 しゃべり 葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) mot
1. 1 2 1 3 2 HMM Rap-style Singing Voice Synthesis Keijiro Saino, 1 Keiichiro Oura, 2 Makoto Tachibana, 1 Hieki Kenmochi 3 an Keiichi Tokua 2 This paper aresses rap-style singing voice synthesis. Since
HITACHI 液晶プロジェクター CP-AX3505J/CP-AW3005J 取扱説明書 -詳細版- 【技術情報編】
B A C E D 1 3 5 7 9 11 13 15 17 19 2 4 6 8 10 12 14 16 18 H G I F J M N L K Y CB/PB CR/PR COMPONENT VIDEO OUT RS-232C LAN RS-232C LAN LAN BE EF 03 06 00 2A D3 01 00 00 60 00 00 BE EF 03 06 00 BA D2 01
取扱説明書 -詳細版- 液晶プロジェクター CP-AW3019WNJ
B A C D E F K I M L J H G N O Q P Y CB/PB CR/PR COMPONENT VIDEO OUT RS-232C LAN RS-232C LAN LAN BE EF 03 06 00 2A D3 01 00 00 60 00 00 BE EF 03 06 00 BA D2 01 00 00 60 01 00 BE EF 03 06 00 19 D3 02 00
IPSJ SIG Technical Report Vol.2019-MUS-123 No.23 Vol.2019-SLP-127 No /6/22 Bidirectional Gated Recurrent Units Singing Voice Synthesi
Bidirectional Gated Recurrent Units Singing Voice Synthesis Using Bidirectional Gated Recurrent Units. [] (HMM) [] [3], [4] Kobe University MEC Company Ltd. (Text to Speech: TTS) [5].. 3Hz Hz c 9 Information
1/68 A. 電気所 ( 発電所, 変電所, 配電塔 ) における変圧器の空き容量一覧 平成 31 年 3 月 6 日現在 < 留意事項 > (1) 空容量は目安であり 系統接続の前には 接続検討のお申込みによる詳細検討が必要となります その結果 空容量が変更となる場合があります (2) 特に記載
1/68 A. 電気所 ( 発電所, 変電所, 配電塔 ) における変圧器の空き容量一覧 平成 31 年 3 月 6 日現在 < 留意事項 > (1) 空容量は目安であり 系統接続の前には 接続検討のお申込みによる詳細検討が必要となります その結果 空容量が変更となる場合があります (2) 特に記載のない限り 熱容量を考慮した空き容量を記載しております その他の要因 ( 電圧や系統安定度など ) で連系制約が発生する場合があります
007 0 ue ue 6 67 090 b 6666 D 666 0 6 6 0 0 0 4 0 6 7 6 6706 00000 00000 69 000040 000040 0040 0040 000040 000040 0040 0040 674 00000 70 00000 0 00000
EDOGAWA ITY Y @ Y 60 7 66997 00 00 00 00 600 000 000 4900 900 700 000 f 004000 00 000 7f 70g 0 0 007 0 ue ue 6 67 090 b 6666 D 666 0 6 6 0 0 0 4 0 6 7 6 6706 00000 00000 69 000040 000040 0040 0040 000040
空き容量一覧表（154kV以上）
1/3 A. 電気所 ( 発電所, 変電所, 配電塔 ) における変圧器の空き容量 覧 < 留意事項 > (1) 空容量は 安であり 系統接続の前には 接続検討のお申込みによる詳細検討が必要となります その結果 空容量が変更となる場合があります (2) 熱容量を考慮した空き容量を記載しております その他の要因 ( や系統安定度など ) で連系制約が発 する場合があります (3) 表 は 既に空容量がないため
2/8 一次二次当該 42 AX 変圧器 なし 43 AY 変圧器 なし 44 BA 変圧器 なし 45 BB 変圧器 なし 46 BC 変圧器 なし
1/8 A. 電気所 ( 発電所, 変電所, 配電塔 ) における変圧器の空き容量一覧 < 留意事項 > (1) 空容量は目安であり 系統接続の前には 接続検討のお申込みによる詳細検討が必要となります その結果 空容量が変更となる場合があります (2) 特に記載のない限り 熱容量を考慮した空き容量を記載しております その他の要因 ( や系統安定度など ) で連系制約が発生する場合があります (3)
2001 Mg-Zn-Y LPSO(Long Period Stacking Order) Mg,,,. LPSO ( ), Mg, Zn,Y. Mg Zn, Y fcc( ) L1 2. LPSO Mg,., Mg L1 2, Zn,Y,, Y.,, Zn, Y Mg. Zn,Y., 926, 1
Mg-LPSO 2566 2016 3 2001 Mg-Zn-Y LPSO(Long Period Stacking Order) Mg,,,. LPSO ( ), Mg, Zn,Y. Mg Zn, Y fcc( ) L1 2. LPSO Mg,., Mg L1 2, Zn,Y,, Y.,, Zn, Y Mg. Zn,Y., 926, 1 1,.,,., 1 C 8, 2 A 9.., Zn,Y,.
HITACHI 液晶プロジェクター CP-EX301NJ/CP-EW301NJ 取扱説明書 -詳細版- 【技術情報編】 日本語
A B C D E F G H I 1 3 5 7 9 11 13 15 17 19 2 4 6 8 10 12 14 16 18 K L J Y CB/PB CR/PR COMPONENT VIDEO OUT RS-232C RS-232C RS-232C Cable (cross) LAN cable (CAT-5 or greater) LAN LAN LAN LAN RS-232C BE
学習の手順
NAVI 2 MAP 3 ABCD EFGH D F ABCD EFGH CD EH A ABC A BC AD ABC DBA BC//DE x 4 a //b // c x BC//DE EC AD//EF//BC x y AD DB AE EC DE//BC 5 D E AB AC BC 12cm DE 10 AP=PB=BR AQ=CQ BS CS 11 ABCD 1 C AB M BD P
05‚å™J“LŁñfi~P01-06_12/27
2005 164 FFFFFFFFF FFFFFFFFF 2 3 4 5 6 7 8 g a 9 f a 10 g e g 11 f g g 12 a g g 1 13 d d f f d 14 a 15 16 17 18 r r 19 20 21 ce eb c b c bd c bd c e c gf cb ed ed fe ed g b cd c b 22 bc ff bf f c f cg
paper.dvi
59 6 2003 pp. 1 11 1 43.72.Kb * 1 2 3 1. 2 2 1 1 1 [1] Person Recognition for News Videos through Multimodal Interaction, by Masakiyo Fujimoto, Yasuo Ariki and Shuji Doshita. 1 ATR 2 3 [email protected]
H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)
![H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)](/thumbs/92/108069141.jpg "H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)")
72 12 2016 pp. 777 782 777 * 43.60.Pt; 43.38.Md; 43.60.Sx 1. 1 2 [1 8] Flexible acoustic interface based on 3D sound reproduction. Yosuke Tatekura (Shizuoka University, Hamamatsu, 432 8561) 2. 2.1 3 M
補足情報
1 危 険 警 告 注 意 2 3 4 5 6 7 8 1 2 3 4 5 9 6 7 8 9 10 10 1 2 11 1 12 1 2 13 3 4 14 1 2 15 3 4 5 16 1 2 3 17 1 2 3 4 18 19 20 21 22 23 1 2 3 4 5 24 6 7 8 9 10 25 26 27 28 6 1 2 7 8 9 3 4 5 29 1 2 警 告 3 4 5
IPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and
MIDI 1 2 3 2 1 Modeling Performance Indeterminacies for Polyphonic Midi Score Following and Its Application to Automatic Accompaniment Nakamura Eita 1 Yamamoto Ryuichi 2 Saito Yasuyuki 3 Sako Shinji 2
音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst
1,a) 1 1 1 deep neural netowrk(dnn) (HMM) () GMM-HMM 2 3 (CSJ) 1. DNN [6]. GPGPU HMM DNN HMM () [7]. [8] [1][2][3] GMM-HMM Gaussian mixture HMM(GMM- HMM) MAP MLLR [4] [3] DNN 1 1 triphone bigram [5]. 2
10_08.dvi
476 67 10 2011 pp. 476 481 * 43.72.+q 1. MOS Mean Opinion Score ITU-T P.835 [1] [2] [3] Subjective and objective quality evaluation of noisereduced speech. Takeshi Yamada, Shoji Makino and Nobuhiko Kitawaki
取扱説明書 [F-08D]
![取扱説明書 [F-08D]](/thumbs/48/23832237.jpg "取扱説明書 [F-08D]")
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 a bc d a b c d 17 a b cd e a b c d e 18 19 20 21 22 a c b d 23 24 a b c a b c d e f g a b j k l m n o p q r s t u v h i c d e w 25 d e f g h i j k l m n o p q r s
Run-Based Trieから構成される 決定木の枝刈り法
Run-Based Trie 2 2 25 6 Run-Based Trie Simple Search Run-Based Trie Network A Network B Packet Router Packet Filtering Policy Rule Network A, K Network B Network C, D Action Permit Deny Permit Network
000 001
all-round catalogue vol.2 000 001 002 003 AA0102 AA0201 AA0701 AA0801 artistic brushes AA0602 AB2701 AB2702 AB2703 AB2704 AA0301 AH3001 AH3011 AH3101 AH3201 AH3111 AB3201 AB3202 AB2601 AB2602 AB0701 artistic
2 HMM HTK[2] 3 left-to-right HMM triphone MLLR 1 CSJ 10 1 : 3 1: GID AM/CSJ-APS/hmmdefs.gz
![2 HMM HTK[2] 3 left-to-right HMM triphone MLLR 1 CSJ 10 1 : 3 1: GID AM/CSJ-APS/hmmdefs.gz](/thumbs/89/99052459.jpg "2 HMM HTK[2] 3 left-to-right HMM triphone MLLR 1 CSJ 10 1 : 3 1: GID AM/CSJ-APS/hmmdefs.gz")
Ver.1.0 2004/3/23 : : 1 1 2 2 2.1..................................... 3 2.2..................................... 5 2.3........................... 5 2.4.............................. 7 2.5............................
欧州特許庁米国特許商標庁との共通特許分類 CPC (Cooperative Patent Classification) 日本パテントデータサービス ( 株 ) 国際部 2019 年 7 月 31 日 CPC 版が発効します 原文及び詳細はCPCホームページのCPC Revision
欧州特許庁米国特許商標庁との共通特許分類 CPC (Cooperative Patent Classification) 日本パテントデータサービス ( 株 ) 国際部 2019 年 7 月 31 日 CPC 2019.08 版が発効します 原文及び詳細はCPCホームページのCPC Revisions(CPCの改訂 ) をご覧ください https://www.cooperativepatentclassification.org/cpcrevisions/noticeofchanges.html
Ł½’¬24flNfix+3mm-‡½‡¹724
571 0.0 31,583 2.0 139,335 8.9 310,727 19.7 1,576,352 100.0 820 0.1 160,247 10.2 38,5012.4 5,7830.4 9,5020.6 41,7592.7 77,8174.9 46,425 2.9 381,410 24.2 1,576,352 100.0 219,332 13.9 132,444 8.4 173,450
2 Excel =sum( ) =average( ) B15:D20 : $E$26 E26 $ =A26*$E$26 $ $E26 E$26 E$26 $G34 $ E26 F4
1234567 0.1234567 = 2 3 =2+3 =2-3 =2*3 =2/3 =2^3 1:^, 2:*/, 3:+- () =2+3*4 =(2+3)*4 =3*2^2 =(3*2)^2 =(3+6)^0.5 A12 =A12+B12 ( ) ( )0.4 ( 100)0.9 % 1 2 Excel =sum( ) =average( ) B15:D20 : $E$26 E26 $ =A26*$E$26
1122 1015 1 Voices 11 11 1 1 1 1 1 1 7 3 4 3 4 3 4 1 1 1 1 1 e 1 f dd 1 d 1 1 1 1 de 1 f 1 d b b bb ef f bb 1 1 882-1111 882-1160 1 1 a 6 1 1 1 f 1 1 c 1 f 1 1 f 1 cf 1 bf 1 1 1 1 a 1 g 1 g 1 af g 1 11
() () () () () 175 () Tel Fax
JPCA-PE04-02-01-02-01S JPCA PE04-02-01-02-01S 2005 () () () () () 175 () 167-0042 3122 2 Tel 03-5310-2020Fax [email protected] Detail Specification for PT Optical Module 1 PT PT 12 Optoelectronic
(4) P θ P 3 P O O = θ OP = a n P n OP n = a n {a n } a = θ, a n = a n (n ) {a n } θ a n = ( ) n θ P n O = a a + a 3 + ( ) n a n a a + a 3 + ( ) n a n
3 () 3,,C = a, C = a, C = b, C = θ(0 < θ < π) cos θ = a + (a) b (a) = 5a b 4a b = 5a 4a cos θ b = a 5 4 cos θ a ( b > 0) C C l = a + a + a 5 4 cos θ = a(3 + 5 4 cos θ) C a l = 3 + 5 4 cos θ < cos θ < 4
美唄市広報メロディー2014年1月号
1 2014 [email protected] January May September October November December February March June July August April BIBAI CITY INFORMATION http://db.net-bibai.co.jp/bibai/
1 1 tf-idf tf-idf i
14 A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles 1055104 2003 1 31 1 1 tf-idf tf-idf i Abstract A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles
21 1 1 1 2 2 5 7 9 11 13 13 14 18 18 20 28 28 29 31 31 34 35 35 36 37 37 38 39 40 56 66 74 89 99 - ------ ------ -------------- ---------------- 1 10 2-2 8 5 26 ( ) 15 3 4 19 62 2,000 26 26 5 3 30 1 13
Number
2009 Sports Graphic Number Changing perceptions of women in the sports space of contemporary Japan :Images of women in Sports Graphic number 5008A0112 Edamoto, Kazumasa 8 9 10 1980 1984 15 15 21 25 27
欧州特許庁米国特許商標庁との共通特許分類 CPC (Cooperative Patent Classification) 日本パテントデータサービス ( 株 ) 国際部 2019 年 1 月 17 日 CPC 版のプレ リリースが公開されました 原文及び詳細はCPCホームページの C
欧州特許庁米国特許商標庁との共通特許分類 CPC (Cooperative Patent Classification) 日本パテントデータサービス ( 株 ) 国際部 2019 年 1 月 17 日 CPC 2019.02 版のプレ リリースが公開されました 原文及び詳細はCPCホームページの CPC Revisions(CPCの改訂 ) 内のPre-releaseをご覧ください http://www.cooperativepatentclassification.org/cpcrevisions/prereleases.html
Catalog No.AR006-e DIN EN ISO 9001 JIS Z 9901 Certificate: 販売終了
Catalog No.AR006-e DIN EN ISO 9001 JIS Z 9901 Certificate:09 100 5919 DJ!0 DF DF @3 q w e 130 230 TR RA 0H R 130 230 RA TR R R RA 0.02MPa RA 130 230 130 230 R 0.06MPa RA 0.15MPa q R #1 TR #6 I N D E X
取扱説明書 ［F-12C］
F-12C 11.7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 a bc b c d d a 15 a b cd e a b c d e 16 17 18 de a b 19 c d 20 a b a b c a d e k l m e b c d f g h i j p q r c d e f g h i j n o s 21 k l m n o p q r s a X
¥ì¥·¥Ô¤Î¸À¸ì½èÍý¤Î¸½¾õ
2013 8 18 Table of Contents = + 1. 2. 3. 4. 5. etc. 1. ( + + ( )) 2. :,,,,,, (MUC 1 ) 3. 4. (subj: person, i-obj: org. ) 1 Message Understanding Conference ( ) UGC 2 ( ) : : 2 User-Generated Content [
VoiceMaker-1.1 ― HMM音声合成用音響モデルの構築
VoiceMaker-1.1 HMM / [email protected] 2 VoiceMaker-1.1 HMM 1 Galatea Toolkit (GalateaTalk) HTS *1 GalateaTalk HTS 1.1 Linux PC CPU CPU 500 512MB 503?? HTK HMM HTS HMM HTK SPTK Julius get f0s F 0
TCX γ 0.9,, H / H, [4], 3. 3., ( /(,,,,,,, Mel Log Spectrum Approximation (MLSA [5],, [6], [7].,,,,,,, (,,, 3.,,,,,,,, sinc,,, [8], W, ( Y ij Y ij W l
![TCX γ 0.9,, H / H, [4], 3. 3., ( /(,,,,,,, Mel Log Spectrum Approximation (MLSA [5],, [6], [7].,,,,,,, (,,, 3.,,,,,,,, sinc,,, [8], W, ( Y ij Y ij W l](/thumbs/91/106039521.jpg "TCX γ 0.9,, H / H, [4], 3. 3., ( /(,,,,,,, Mel Log Spectrum Approximation (MLSA [5],, [6], [7].,,,,,,, (,,, 3.,,,,,,,, sinc,,, [8], W, ( Y ij Y ij W l")
,a,b,c,d,e,,,,,,,, TCX.,, (VoIP,,, 3GPP Extended Adaptive Multi-Rate Wideband (AMR- WB+ MPEG-D Unified Speech and Audio Coding (USAC [], [],,,, AMR-WB+ USAC, Transform Coded exitation (TCX, TCX NTT a [email protected]
Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information
![Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information](/thumbs/91/104959916.jpg "Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information")
Vol.54 No.7 1937 1950 (July 2013) 1,a) 2012 11 1, 2013 4 5 1 Similar Sounds Sentences Generator Based on Morphological Analysis Manner and Low Class Words Masaaki Kanakubo 1,a) Received: November 1, 2012,
‚å™J‚å−w“LŁñfi~P01†`08
156 2003 2 3 4 5 6 7 8 9 c f c a g 10 d c d 11 e a d 12 a g e 13 d fg f 14 g e 15 16 17 18 19 20 21 db de de fg fg g gf b eb g a a e e cf b db 22 d b e ag dc dc ed gf cb f f e b d ef 23 f fb ed e g gf
HTS Slides
統計的機械学習問題としての 音声合成 徳田恵一 ( 名古屋工業大学 ) 音学シンポジウム 2013, 東京 2013 年 5 月 11 日 はじめに ルールベース : フォルマント合成 (~ 90s) 手動ルールによる各音素の素片を構築 コーパスベース : 波形接続型音声合成 ( 90s~) 音声データベースから音声素片を接続し合成 単一インベントリ : ダイフォン音声合成 複数インベントリ : 単位選択型音声合成
日立液晶プロジェクター CP-AW2519NJ 取扱説明書- 詳細版-
PAGE UP DOWN D- ESC ENTER 1 1 2 2 3 COMPUTER IN1 USB TYPE A DC5V 0.5A USB TYPE B HDMI COMPUTER IN2 LAN CONTROL MONITOR OUT MIC AUDIO IN1 AUDIO IN3 AUDIO OUT R R L L S-VIDEO AUDIO IN2 VIDEO PAGE UP DOWN
A G A G A G 4 1 1 2 3 4 5 6 7 110119118 b A G C G 4 1 7 * * G A C b a HIKJ K J L f B c g 9 K c d g e 7 G 7 1 G 1 aa g g g c L M G L H G G 4 aa c c A a c CB B C A G f A G f G 9 8 1 2
pp d 2 * Hz Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1
72 12 2016 pp. 739 748 739 43.60.+d 2 * 1 2 2 3 2 125 Hz 0.3 0.8 2 125 Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1. 1.1 PSS [1] [2 4] 2 Wind-induced noise reduction
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
![2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server](/thumbs/91/107223763.jpg "2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server")
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
取扱説明書［F-09E］
13.9 ISSUE DATE: NAME: PHONE NUMBER: MAIL ADDRESS: F-09E e 1 2 1 2 3 4 5 6 7 8 9 10 11 a b c d a b c d a b cd e a b c d e 12 13 14 a b a b a 15 b c d 16 c d e f g h i n o p q r x a b j k l n s n t u v
a (a + ), a + a > (a + ), a + 4 a < a 4 a,,, y y = + a y = + a, y = a y = ( + a) ( x) + ( a) x, x y,y a y y y ( + a : a ) ( a : a > ) y = (a + ) y = a
[] a x f(x) = ( + a)( x) + ( a)x f(x) = ( a + ) x + a + () x f(x) a a + a > a + () x f(x) a (a + ) a x 4 f (x) = ( + a) ( x) + ( a) x = ( a + a) x + a + = ( a + ) x + a +, () a + a f(x) f(x) = f() = a
BD = a, EA = b, BH = a, BF = b 3 EF B, EOA, BOD EF B EOA BF : AO = BE : AE, b : = BE : b, AF = BF = b BE = bb. () EF = b AF = b b. (2) EF B BOD EF : B
2000 8 3.4 p q θ = 80 B E a H F b θ/2 O θ/2 D A B E BD = a, EA = b, BH = a, BF = b 3 EF B, EOA, BOD EF B EOA BF : AO = BE : AE, b : = BE : b, AF = BF = b BE = bb. () EF = b AF = b b. (2) EF B BOD EF :
DPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
Crossover&Fusion_vol3~4.indd
11 29 11 8 2017 No.01 A B C D E F G H I J K Jaco Pastorius No.02 A B C D E F G H I J K No.03 No.04 A B C D E F G 1 A B C D E F G H 2017 11 8 11 29 No.05 No.06 ABC D E F G A BC D E F G No.07 A B C D E F