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1 1
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3 Google voice search - - Google translation - - Siri, - - 3
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8 ( ) - 81 CD (CD =700 ) - - Sub-word 8
9 - sub-word - - /a/ /k/ /i/ /t/ /k/ - /t/ /k/ /t/ /k/ - - /a/ /ka/ /i/ /so/ /ra/ /a/ /o/ /i/ /k/ /s/ /r/ 9
10 International Phonetic Alphabet (IPA) 10
11 IPA 11
12 - (Wikipedia ) - Festival stops, 10 fricatives, 3 nasals, 4 liquids)
13 a ax abbreviate ax b r iy v iy ey t ability ax b ih l ix t iy able ey b el ably ey b l iy about ax b aw t above ax b ah v abruptly ax b r ah p t l iy 13
14 JEITA 14
15 15
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17 17
18 - - - : 18
19 - 3 IPA Jont B. Allen James L.Flanagan and Mark A.Hasegawa-Johnson, Speech Analysis Synthesis and Perception, Springer-Verlag,
20 /b/, /d/, /g/ - - /b/ /d/ /g/ Delattre, P. C., A. M. Liberman and F. S. Cooper (1955) Acoustic Loci and Transitional Cues for Consonants. JASA vol. 27, no
21 /l/, /r/ /right/ -> /light/ 21
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26 - - - ( ) Parametric Artificial Talker (PAT) (Walter Lawrence, Edinburgh, 1950s-1960s) - OVE (Gunner Fant, Sweden 1960s) - MITalk (1970s), KLATTalk, DECTalk Example 1: MITalk 26
27 Sparte (Courbon and Emerald, France telecom, 82) - Yamaha, Japan, 2003 Examples: diphone synthesizer Sparte: A text-to-speech machine using synthesis by diphones Courbon, J.-L.; Emerard, F.; Proc. ICASSP-82 27
28 CHATR (Hunt and Black, ATR, Japan, 95) - Festival (Black, CSTR, Edinburgh, UK, 97) - AT&T Natural voice (USA) Examples: unit selection synthesizer 28
29 Hidden Markov Model, HMM Heiga Zen, Takashi Nose, Junichi Yamagishi, Shinji Sako, Keiichi Tokuda, The HMM-based speech synthesis system (HTS) version 2.0, SSW 6, pp , Aug Keiichi Tokuda, Yoshihiko Nankaku, Tomoki Toda, Heiga Zen, Junichi Yamagishi, and Keiichiro Oura Speech Synthesis Based on Hidden Markov Models Proceedings of The IEEE,
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31 HMM r t ai
32 speech parameter time 32
33 HMM - - HTS (H Triple S 2002 ) - Interspeech % HTS - - NTT 12 - HOYA VoiceText Micro SDK - Nuance Communication Nuance Vocalizer - KDDI N2 TTS - Google Android OS 33
34
35 MB
36 - - - Pascal Belin Vocal attractiveness increases by averaging, Current Biology, 20, January 26, r=
37 37
38 10 38
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40 HMM
41 Regression class 1 M. Tachibana, J. Yamagishi, T. Masuko, T. Kobayashi, Speech Synthesis with Various Emotional Expressions and Speaking Styles by Style Interpolation and Morphing, IEICE Trans. Information and Systems, E88-D, no.11, pp , November
42 O. Watts, J. Yamagishi, S. King, K. Berkling, Synthesis of Child Speech with HMM Adaptation and Voice Conversion IEEE Audio, Speech, & Language Processing, vol.18, issue.5, pp , July 2010 M. Pucher, D. Schabus, J. Yamagishi, F. Neubarth Modeling and Interpolation of Austrian German and Viennese Dialect in HMM-based Speech Synthesis, Speech Communication, Volume 52, Issue 2, Pages , February
43 J. Yamagishi, B. Usabaev, S. King, O. Watts, J. Dines, J. Tian, R. Hu, Y. Guan, K. Oura, K. Tokuda, R. Karhila, M. Kurimo, Thousands of Voices for HMM-based Speech Synthesis -- Analysis and Application of TTS Systems Built on Various ASR Corpora, IEEE Trans. Audio, Speech, & Language Processing, vol.18, issue.5, pp , July
44 Good morning S2ST system 44
45 Target speaker Average voice 5 sentences 50 sentences 2000 sentences 45
46 % improvement in relative 38% improvement in relative % improvement 38% improvement in relative in relative Word accuracy rate (%) Word accuracy rate (%) N L L E 20 N L L E Speech modulated noise Competing speaker C. Valentini-Botinhao, J. Yamagishi, S. King, Mel cepstral coefficient modification based on the Glimpse Proportion measure for improving the intelligibility of HMM-generated synthetic speech in noise, Proc Interspeech 2012 C. Valentini-Botinhaoa, J. Yamagishia, S. Kinga, R. Maiab,"Intelligibility enhancement of HMM-generated speech in additive noise by modifying Mel cepstral coefficients to increase the Glimpse Proportion" Computer & Speech Language,
47 ALS 9
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11 22 33 12 23 1 2 3, 1 2, U2 3 U 1 U b 1 (o t ) b 2 (o t ) b 3 (o t ), 3 b (o t ) MULTI-SPEAKER SPEECH DATABASE Training Speech Analysis Mel-Cepstrum, logf0 /context1/ /context2/... Context Dependent
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
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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
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
Vol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information
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1,a) 1 1 WORLD F0 Vocaloid F0 ipad 1. Vocaloid [1] UTAU *1 Vocaloid Vocaloid F0 VocaListener [2], [3] Vocaloid *2 VocaListener Vocaloid 1 University of Yamanashi a) g16tk018@yamanashi.ac.jp *1 http://utau2008.web.fc2.com/
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
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1 1 CodeDrummer CodeMusician CodeDrummer Fig. 1 Overview of proposal system c
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1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 657 8531 1 1 E-mail: {soda,matsubara}@ws.cs.kobe-u.ac.jp, {masa-n,shinsuke,shin,yosimoto}@cs.kobe-u.ac.jp,
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