LSP (Line Spectrum Pair, LSF: Line Spectrum Frequencies) [19] [20] LSP CODE [21], [22] VOCODER [23] 2. 3 STRAIGHT VOCODER STRAIGHT [24] STRAIGHT [25]

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1 一般社団法人電子情報通信学会信学技報 THE INSTITUTE OF ELECTRONICS, IEICE Technical Report INFORMATION AND COMMUNICATION ENGINEERS EA ( ) [ ]VOCODER kawahara@sys.wakayama-u.ac.jp 80 VOCODER VOCODER [Invited]Revisiting VOCODER Why I intentionally discard the original phase of the original speech? Hideki KAWAHARA Wakayama University 930 Sakae-dani, Wakayama, Wakayama, Japan kawahara@sys.wakayama-u.ac.jp Abstract VOCODER is a framework invented for narrow band communication about 80 years ago. It has been providing a productive basis for speech research and applications. It also will play another productive roles in the age of deep learning, a rapidly expanding research and deployment framework. I would like to introduce a perspective on new roles of VOCODER, based on reviewing of research tools, which I developed and am currently developing. Key words speech, phase, spectrum, instantaneous frequency, group delay, sampling, deep learning VOCODER [1] VOCODER 80 [2] VOCODER [3] [5] [6] VOCODER VOCODER 2. VOCODER VOCODER 2. 1 VOCODER [7], [8] [9] [10] pattern playback [11], [12] [13], [14] 2. 2 [15] VOCODER VOCODER LPC (Linear Predictive Coding) [16], PARCOR (PARtial autocorrelation) [14], [17], CSM(Composite Sinusoidal Modeling) [18], 21 This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere. Copyright 2017 by IEICE

[37] [38] Google scholar STRAIGHT 2017 6 2,000 20 STRAIGHT WORLD [39] STRAIGHT [4]Mel cepstrum [40] 2. 4 WaveNet [2] WaveNet WaveNet μ-law [41] 256 VOCODER VOCODER [27] 1 Fig.

2 LSP (Line Spectrum Pair, LSF: Line Spectrum Frequencies) [19] [20] LSP CODE [21], [22] VOCODER [23] 2. 3 STRAIGHT VOCODER STRAIGHT [24] STRAIGHT [25] [26], [27] STRAIGHT VOCODER VOCODER STRAIGHT [28], [29] STRAIGHT TANDEM-STRAIGHT [30] [31] [33] [34], [35] STRAIGHT( ) [5], [36] STRAIGHT [37] [38] Google scholar STRAIGHT , STRAIGHT WORLD [39] STRAIGHT [4]Mel cepstrum [40] 2. 4 WaveNet [2] WaveNet WaveNet μ-law [41] 256 VOCODER VOCODER [27] 1 Fig. 1 Demonstration movie for phase perception [6], [42], [43] VOCODER Google UK [44], [45] [46], [47] 3. SparkNG SparkNG [48], [49] 30 [50] SparkNG GUI 3. 1 [51] [52] [52] MATLAB 1 Schroeder [53] 1)cos 2)sin 3) sin cos 4)Schroeder 5)0 2π 1 1), 2), 3) [52] 50 Hz 400 Hz 20 db [54] 22

Fig. 3 3 Realtime visualization of the vocal tract shape. 2 ERB N number 1/3 Fig.

Upper image shows ERB N number-based representation.

2 ERB N number [55] [56] 2 ERB N number 1/3 /aiueo/ ERB N number 1/3 FFT(Fast Fourier

3 Fig. 3 3 Realtime visualization of the vocal tract shape. 2 ERB N number 1/3 Fig. 2 Time-frequency representation using non-linear frequency resolution. Upper image shows ERB N number-based representation. Lower image shows 1/3 octave-based representation ERB N number [55] [56] 2 ERB N number 1/3 /aiueo/ ERB N number 1/3 FFT(Fast Fourier Transform) Bark [57] 3. 3 PARCOR SparkNG 3 PARCOR 3 MacBook Pro (Retina, 13- inch, 2.9GHz Intel Core i5) MATLAB (R2017a) 20 fps MATLAB SparkNG GUI GUI GUI 4 GUI 23

Fig. 4 4 Filter manipulation GUI of the speech

5 Glottal source manipulation GUI of the speech

model [58] L-F model L-F model t p t p t a t c 4

t a +6 dboct modal, breathy, vocal fry [59] 3. 4.

4 Fig. 4 4 Filter manipulation GUI of the speech production simulator. 44,100 Hz LSP Fant L-F 5 Fig. 5 Glottal source manipulation GUI of the speech production simulator. model [58] L-F model L-F model t p t p t a t c 4 5 GUI L-F model 3 L-F model (t p,t e,t a,t c) 5 t a +6 dboct modal, breathy, vocal fry [59] L-F model L-F model [60] Fujisaki-Ljungqvist model [61] 24

5 [62] cos [46] cos 80 db [47] [44] L-F model VOCODER [44] [63] SparkNG 4. [42], [43] VOCODER VOCODER [3], [5] WaveNet VOCODER 16K12464 (B)15H02726 VOCODER ATR STRAIGHT [1] H. Dudley, Remaking Speech, The Journal of the Acoustical Society of America, vol.11, no.2, pp , [2] A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, WaveNet: A generative model for raw audio, arxiv preprint arxiv: , pp.1 15, [3] Y.C. Eldar and T. Michaeli, Beyond bandlimited sampling, IEEE Signal Processing Magazine, vol.26, no.3, pp.48 68, may [4] vol.73 no.9 p [ ] [5] , ( 15-May-2017) [6] M. Blaauw and J. Bonada, A neural parametric singing synthesizer, arxiv preprint arxiv: , pp.1 9, apr [7] T. Chiba and M. Kajiyama, The Vowel, Its Nature and Structure, Tokyo-Kaiseikan, [8] vol.5 no.2 pp [9] G. Fant, Acoustic theory of speech production: with calculations based on X-ray studies of Russian articulations, vol.2, Walter de Gruyter, [originally, 1960, Mouton]. [10] [ ] Sona-Graph vol.11 no.1 pp [11] F.S. Cooper, A.M. Lieberman, and J.M. Borst, The interconversion of audible and visible patterns as a basis for research in the perception of speech, Proc. N. A. S., vol.37, pp , [12] pp.1 Q 28, [13] C.G. Bell, H. Fujisaki, J.M. Heinz, K.N. Stevens, and A.S. House, Reduction of Speech Spectra by Analysis-by- Synthesis Techniques, The Journal of the Acoustical Society of America, vol.33, no.12, pp , [14] [ ] vol.19 no.7 pp [15] vol.53a no.1 pp [16] B.S. Atal and S.L. Hanauer, Speech analysis and synthesis by linear prediction of the speech wave, The Journal of the Acoustical Society of America, vol.50, no.2b, pp , [17] pp [18] vol.j64-a no.2 pp [19] (LSP) A vol.64 no.8 pp [20] vol.j83-a no.11 pp [21] M. Schroeder and B.S. Atal, Code-excited linear prediction (CELP): High-quality speech at very low bit rates, Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP 85., vol.10ieee, pp [22] ITU-T, G.729 : Coding of speech at 8 kbit/s using conjugate-structure algebraic-code-excited linear prediction (CS-ACELP), [started 1996, In force 2012]. [23] A.S. Spanias, Speech coding: A tutorial review, Proceedings of the IEEE, vol.82, no.10, pp , [24] H. Kawahara, I. Masuda-Katsuse, and A. decheveigné, Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequencybased F0 extraction, Speech Communication, vol.27, no.3-4, pp , [25] A.S. Bregman, et al., Auditory scene analysis, vol.10, Cambridge, ma: mit press, [26] vocoder: Straight (< > ), vol.54 no.7 pp [27] Vocoder : straight, vol.63 no.8 pp [28] C. Liu and D. Kewley-Port, Vowel formant discrimination for high-fidelity speech, The Journal of the Acoustical Society of America, vol.116, no.2, pp , [29] P.F. Assmann and W.F. Katz, Synthesis fidelity and timevarying spectral change in vowels, The Journal of the Acoustical Society of America, vol.117, no.2, pp ,

6 [30] H. Kawahara, M. Morise, T. Takahashi, R. Nisimura, T. Irino, and H. Banno, TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0 and aperiodicity estimation, ICASSP 2008, pp , Las Vegas, [31] H. Kawahara and H. Matsui, Auditory morphing based on an elastic perceptual distance metric in an interferencefree time-frequency representation, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol.1, pp , [32] H. Kawahara, R. Nisimura, T. Irino, M. Morise, T. Takahashi, and H. Banno, Temporally variable multi-aspect auditory morphing enabling extrapolation without objective and perceptual breakdown, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp , [33] H. Kawahara, M. Morise, Banno, and V.G. Skuk, Temporally variable multi-aspect N-way morphing based on interference-free speech representations, ASPIPA ASC 2013, p.0s28.02, [34] L. Bruckert, P. Bestelmeyer, M. Latinus, J. Rouger, I. Charest, G.A. Rousselet, H. Kawahara, and P. Belin, Vocal Attractiveness Increases by Averaging, Current Biology, vol.20, no.2, pp , [35] S.R. Schweinberger, C. Casper, N. Hauthal, J.M. Kaufmann, H. Kawahara, N. Kloth, D.M.C. Robertson, A.P. Simpson, and R. Zäske, Auditory Adaptation in Voice Perception, Current Biology, vol.18, pp , [36] M. Unser, Sampling-50 years after Shannon, Proceedings of the IEEE, vol.88, no.4, pp , apr [37] H. Zen, K. Tokuda, and A.W. Black, Statistical parametric speech synthesis, Speech Communication, vol.51, no.11, pp , nov [38] H. Zen, T. Toda, M. Nakamura, and K. Tokuda, Details of the nitech hmm-based speech synthesis system for the blizzard challenge 2005, IEICE transactions on information and systems, vol.90, no.1, pp , [39] M. Morise, F. Yokomori, and K. Ozawa, World: A vocoderbased high-quality speech synthesis system for real-time applications, IEICE TRANSACTIONS on Information and Systems, vol.99, no.7, pp , [40] S. Imai, Cepstral analysis synthesis on the mel frequency scale, ICASSP 83. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.8, pp.93 96, Institute of Electrical and Electronics Engineers, Boston, apr [41] ITU-T, G.711 : Pulse code modulation (PCM) of voice frequencies, [42] Y. Saito, S. Takamichi, and H. Saruwatari, Training algorithm to deceive anti-spoofing verification for dnn-based speech synthesis, Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference onieee, pp [43] T. Kaneko, H. Kameoka, N. Hojo, Y. Ijima, K. Hiramatsu, and K. Kashino, Generative adversarial networkbased postfilter for statistical parametric speech synthesis, Proc IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017), pp , [44] H. Kawahara, Y. Agiomyrgiannakis, and H. Zen, Using instantaneous frequency and aperiodicity detection to estimate F0 for high-quality speech synthesis, arxiv preprint arxiv: , pp.1 10, [45] H. Kawahara, Y. Agiomyrgiannakis, and H. Zen, YANG VOCODER: Yet-ANother-Generalized VOCODER. ( ). [46] H. Kawahara, K. Sakakibara, H. Banno, M. Morise, T. Toda, and T. Irino, A new cosine series antialiasing function and its application to aliasing-free glottal source models for speech and singing synthesis, Proc. Interspeech 2017, p., (Accepted: Extended draft: arxiv preprint arxiv: ). [47] H. Kawahara, K. Sakakibara, H. Banno, M. Morise, and T. Toda, A modulation property of time-frequency derivatives of filtered phase and its application to aperiodicity and fo estimation, Proc. Interspeech 2017, p., (Accepted: Extended draft: arxiv preprint arxiv: ). [48] vol.18 no.3 pp [49] H. Kawahara, MATLAB realtime speech tools and voice production tools, ( 20-Feb.-2017). [50],, H ( NTT (1989) ) [51] R. Plomp and H. 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