An overview of voice conversion systems

Speech Communication

Volumn 88, Issue , 2017, Pages 65-82

  Mohammadi, Seyed Hamidreza ^a   Kain, Alexander ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Overview; Survey; Voice conversion

Indexed keywords

COMPUTER SIMULATION; SURVEYING;

EXISTING SYSTEMS; LINGUISTIC INFORMATION; OVERVIEW; REAL-WORLD; SPEECH QUALITY; SPEECH SIGNALS; TARGET SPEAKER; VOICE CONVERSION;

COMPUTER APPLICATIONS;

EID: 85010399617     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2017.01.008     Document Type: Review

References (305)

1. Abe, M., Nakamura, S., Shikano, K., Kuwabara, H., Voice conversion through vector quantization. Proceedings of the ICASSP, 1988.
2. Agiomyrgiannakis, Y., VOCAINE the vocoder and applications in speech synthesis. Proceedings of the ICASSP, 2015.
3. Agiomyrgiannakis, Y., Rosec, O., ARX-LF-based source-filter methods for voice modification and transformation. Proceedings of the ICASSP, 2009.
4. Aihara, R., Nakashika, T., Takiguchi, T., Ariki, Y., Voice conversion based on non-negative matrix factorization using phoneme-categorized dictionary. Proceedings of the ICASSP, 2014.
5. Aihara, R., Takashima, R., Takiguchi, T., Ariki, Y., Individuality-preserving voice conversion for articulation disorders based on non-negative matrix factorization. Proceedings of the ICASSP, 2013.
6. AIHARA, R., TAKIGUCHI, T., ARIKI, Y., Activity-mapping non-negative matrix factorization for exemplar-based voice conversion. Proceedings of the ICASSP, 2015.
7. Aihara, R., Takiguchi, T., Ariki, Y., Many-to-many voice conversion based on multiple non-negative matrix factorization. Proceedings of the INTERSPEECH, 2015.
8. Aihara, R., Ueda, R., Takiguchi, T., Ariki, Y., Exemplar-based emotional voice conversion using non-negative matrix factorization. Proceedings of the APSIPA, 2014, 10.1109/APSIPA.2014.7041640.
9. Alegre, F., Amehraye, A., Evans, N., Spoofing countermeasures to protect automatic speaker verification from voice conversion. Proceedings of the ICASSP, 2013.
10. Anumanchipalli, G.K., Prahallad, K., Black, A.W., Festvox: Tools for creation and analyses of large speech corpora. Workshop on Very Large Scale Phonetics Research, UPenn, Philadelphia, 2011.
11. Arslan, L.M., Speaker transformation algorithm using segmental codebooks (STASC). Speech Commun. 28:3 (1999), 211–226.
12. Arslan, L.M., Talkin, D., Voice conversion by codebook mapping of line spectral frequencies and excitation spectrum. Proceedings of the EUROSPEECH, 1997.
13. Arslan, L.M., Talkin, D., Speaker transformation using sentence HMM based alignments and detailed prosody modification. Proceedings of the ICASSP, 1998.
14. Aryal, S., Felps, D., Gutierrez-Osuna, R., Foreign accent conversion through voice morphing. Proceedings of the INTERSPEECH, 2013.
15. Azarov, E., Vashkevich, M., Likhachov, D., Petrovsky, A., Real-time voice conversion using artificial neural networks with rectified linear units. Proceedings of the INTERSPEECH, 2013.
16. Barra, R., Montero, J.M., Macias-Guarasa, J., Gutiérrez-Arriola, J., Ferreiros, J., Pardo, J.M., On the limitations of voice conversion techniques in emotion identification tasks. Proceedings of the INTERSPEECH, 2007.
17. Benisty, H., Malah, D., Voice conversion using gmm with enhanced global variance. Proceedings of the INTERSPEECH, 2011.
18. Benisty, H., Malah, D., Crammer, K., Sequential voice conversion using grid-based approximation. Proceedings of the IEEEI, 2014.
19. Bi, N., Qi, Y., Application of speech conversion to alaryngeal speech enhancement. IEEE Trans. Speech Audio Process. 5:2 (1997), 97–105.
20. Bonafonte, A., Höge, H., Kiss, I., Moreno, A., Ziegenhain, U., van den Heuvel, H., Hain, H.-U., Wang, X.S., Garcia, M.-N., TC-STAR: Specifications of language resources and evaluation for speech synthesis. Proceedings of the LREC, 2006.
21. Cano, P., Loscos, A., Bonada, J., De Boer, M., Serra, X., Voice morphing system for impersonating in karaoke applications. Proceedings of the ICMC, 2000.
22. Ceyssens, T., Verhelst, W., Wambacq, P., On the construction of a pitch conversion system. Proceedings of the EUSIPCO, 2002.
23. Chappell, D.T., Hansen, J.H., Speaker-specific pitch contour modeling and modification. Proceedings of the ICASSP, 1998.
24. Chen, L.-H., Ling, Z.-H., Dai, L.-R., Voice conversion using generative trained deep neural networks with multiple frame spectral envelopes. Proceedings of the INTERSPEECH, 2014.
25. Chen, L.-H., Ling, Z.-H., Liu, L.-J., Dai, L.-R., Voice conversion using deep neural networks with layer-wise generative training. IEEE/ACM Trans. Audio Speech Language Process. (TASLP) 22:12 (2014), 1859–1872.
26. Chen, L.-H., Ling, Z.-H., Song, Y., Dai, L.-R., Joint spectral distribution modeling using restricted boltzmann machines for voice conversion. Proceedings of the INTERSPEECH, 2013.
27. Chen, L.-H., Liu, L.-J., Ling, Z.-H., Jiang, Y., Dai, L.-R., The USTC system for voice conversion challenge 2016: neural network based approaches for spectrum, aperiodicity and F0 conversion. Proceedings of the INTERSPEECH, 2016.
28. Chen, Y., Chu, M., Chang, E., Liu, J., Liu, R., Voice conversion with smoothed GMM and MAP adaptation. Proceedings of the EUROSPEECH, 2003.
29. Childers, D., Yegnanarayana, B., Wu, K., Voice conversion: Factors responsible for quality. Proceedings of the ICASSP, 1985.
30. Childers, D.G., Glottal source modeling for voice conversion. Speech Commun. 16:2 (1995), 127–138.
31. Childers, D.G., Wu, K., Hicks, D., Yegnanarayana, B., Voice conversion. Speech Commun. 8:2 (1989), 147–158.
32. Correia, M.J.R.F., Anti-Spoofing: Speaker Verification vs. Voice Conversion, 2014, Instituto Superior Técnico Master's Thesis.
33. Del Pozo, A., Young, S., The linear transformation of lf glottal waveforms for voice conversion. Proceedings of the INTERSPEECH, 2008.
34. Desai, S., Black, A.W., Yegnanarayana, B., Prahallad, K., Spectral mapping using artificial neural networks for voice conversion. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 954–964.
35. Doi, H., Toda, T., Nakano, T., Goto, M., Nakamura, S., Singing voice conversion method based on many-to-many eigenvoice conversion and training data generation using a singing-to-singing synthesis system. Proceedings of the APSIPA, 2012.
36. Dutoit, T., Holzapfel, A., Jottrand, M., Moinet, A., Perez, J., Stylianou, Y., Towards a voice conversion system based on frame selection. Proceedings of the ICASSP, 2007.
37. Duxans, H., Voice Conversion applied to Text-to-Speech systems, 2006, Universitat Politecnica de Catalunya, Barcelona, Spain Ph.D. thesis.
38. Duxans, H., Bonafonte, A., Residual conversion versus prediction on voice morphing systems. Proceedings of the ICASSP, 2006.
39. Duxans, H., Bonafonte, A., Kain, A., Van Santen, J., Including dynamic and phonetic information in voice conversion systems. Proceedings of the ICSLP, 2004.
40. Duxans, H., Erro, D., Pérez, J., Diego, F., Bonafonte, A., Moreno, A., Voice conversion of non-aligned data using unit selection. TC-STAR WSST, 2006.
41. En-Najjary, T., Rosec, O., Chonavel, T., A new method for pitch prediction from spectral envelope and its application in voice conversion. Proceedings of the INTERSPEECH, 2003.
42. En-Najjary, T., Rosec, O., Chonavel, T., A voice conversion method based on joint pitch and spectral envelope transformation. Proceedings of the INTERSPEECH, 2004.
43. Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S., Why does unsupervised pre-training help deep learning?. J. Mach. Learn. Res. 11 (2010), 625–660.
44. Erro, D., Alonso, A., Serrano, L., Navas, E., Hernáez, I., Towards physically interpretable parametric voice conversion functions. Advances in Nonlinear Speech Processing, 2013, Springer, 75–82.
45. Erro, D., Alonso, A., Serrano, L., Navas, E., Hernaez, I., Interpretable parametric voice conversion functions based on gaussian mixture models and constrained transformations. Comput. Speech Lang. 30:1 (2015), 3–15.
46. Erro, D., Alonso, A., Serrano, L., Tavarez, D., Odriozola, I., Sarasola, X., Del Blanco, E., Sanchez, J., Saratxaga, I., Navas, E., et al. Ml parameter generation with a reformulated mge training criterion—participation in the voice conversion challenge 2016. Proceedings of the INTERSPEECH, 2016.
47. Erro, D., Moreno, A., Frame alignment method for cross-lingual voice conversion. Proceedings of the INTERSPEECH, 2007.
48. Erro, D., Moreno, A., Weighted frequency warping for voice conversion. Proceedings of the INTERSPEECH, 2007.
49. Erro, D., Moreno, A., Bonafonte, A., INCA algorithm for training voice conversion systems from nonparallel corpora. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 944–953.
50. Erro, D., Moreno, A., Bonafonte, A., Voice conversion based on weighted frequency warping. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 922–931.
51. Erro, D., Navas, E., Hernáez, I., Iterative MMSE estimation of vocal tract length normalization factors for voice transformation. Proceedings of the INTERSPEECH, 2012.
52. Erro, D., Polyakova, T., Moreno, A., On combining statistical methods and frequency warping for high-quality voice conversion. Proceedings of the ICASSP, 2008.
53. Erro, D., Sainz, I., Navas, E., Hernáez, I., Improved HNM-based vocoder for statistical synthesizers. Proceedings of the INTERSPEECH, 2011.
54. Eslami, M., Sheikhzadeh, H., Sayadiyan, A., Quality improvement of voice conversion systems based on trellis structured vector quantization. Twelfth Annual Conference of the International Speech Communication Association, 2011.
55. Fan, B., Lee, S.W., Tian, X., Xie, L., Dong, M., A waveform representation framework for high-quality statistical parametric speech synthesis. Proceedings of the APSIPA, 2015 arXiv preprint arXiv:1510.01443.
56. Fujii, K., Okawa, J., Suigetsu, K., Highindividuality voice conversion based on concatenative speech synthesis. World Academy of Science, Engineering and Technology, 2, 2007, 1.
57. Furui, S., Speaker-independent isolated word recognition using dynamic features of speech spectrum. IEEE Transactions on Acoustics, Speech and Signal Processing 34:1 (1986), 52–59.
58. Garofolo, J.S., Lamel, L.F., Fisher, W.M., Fiscus, J.G., Pallett, D.S., DARPA TIMIT Acoustic-Phonetic Continous Speech Corpus CD-ROM. Nist Speech Disc 1-1.1. 93, 1993, NASA STI, Recon Technical Report N, 27403.
59. Ghorbandoost, M., Sayadiyan, A., Ahangar, M., Sheikhzadeh, H., Shahrebabaki, A.S., Amini, J., Voice conversion based on feature combination with limited training data. Speech Commun. 67 (2015), 113–128.
60. Gillett, B., King, S., Transforming f0 contours. Proceedings of the EUROSPEECH, 2003.
61. Glorot, X., Bengio, Y., Understanding the difficulty of training deep feedforward neural networks. International Conference on Artificial Intelligence and Statistics, 2010, 249–256.
62. Glorot, X., Bordes, A., Bengio, Y., Deep sparse rectifier neural networks. Aistats, 2011.
63. Godoy, E., Koutsogiannaki, M., Stylianou, Y., Assessing the intelligibility impact of vowel space expansion via clear speech-inspired frequency warping. Proceedings of the INTERSPEECH, 2013.
64. Godoy, E., Koutsogiannaki, M., Stylianou, Y., Approaching speech intelligibility enhancement with inspiration from lombard and clear speaking styles. Comput. Speech. Lang. 28:2 (2014), 629–647.
65. Godoy, E., Rosec, O., Chonavel, T., Alleviating the one-to-many mapping problem in voice conversion with context-dependent modelling. Proceedings of the INTERSPEECH, 2009.
66. Godoy, E., Rosec, O., Chonavel, T., On transforming spectral peaks in voice conversion. Proceedings of the SSW, 2010.
67. Godoy, E., Rosec, O., Chonavel, T., Speech spectral envelope estimation through explicit control of peak evolution in time. Proceedings of the ISSPA, 2010.
68. Godoy, E., Rosec, O., Chonavel, T., Spectral envelope transformation using DFW and amplitude scaling for voice conversion with parallel or nonparallel corpora. Proceeding of the INTERSPEECH, 2011.
69. Godoy, E., Rosec, O., Chonavel, T., Voice conversion using dynamic frequency warping with amplitude scaling, for parallel or nonparallel corpora. IEEE Trans. Audio Speech Lang. Process. 20:4 (2012), 1313–1323.
70. Gu, H.-Y., Tsai, S.-F., Improving segmental GMM based voice conversion method with target frame selection. Proceedings of the ISCSLP, 2014.
71. Hanzlíček, Z., Matoušek, J., F0 transformation within the voice conversion framework. Proceedings of the INTERSPEECH, 2007.
72. Hashimoto, M., Higuchi, N., Spectral mapping method for voice conversion using speaker selection and vector field smoothing. Proceedings of the EUROSPEECH, 1995.
73. Hashimoto, M., Higuchi, N., Training data selection for voice conversion using speaker selection and vector field smoothing. Proceedings of the ICSLP, 1996.
74. Helander, E., Nurminen, J., Gabbouj, M., Analysis of lsf frame selection in voice conversion. Proceedings of the SPECOM, 2007.
75. Helander, E., Nurminen, J., Gabbouj, M., Lsf mapping for voice conversion with very small training sets. Proceedings of the ICASSP, 2008.
76. Helander, E., Schwarz, J., Nurminen, J., Silen, H., Gabbouj, M., On the impact of alignment on voice conversion performance. Proceedings of the INTERSPEECH, 2008.
77. Helander, E., Silén, H., Míguez, J., Gabbouj, M., Maximum a posteriori voice conversion using sequential monte carlo methods. Proceedings of the INTERSPEECH, 2010.
78. Helander, E., Silén, H., Virtanen, T., Gabbouj, M., Voice conversion using dynamic kernel partial least squares regression. IEEE Trans. Audio Speech Lang. Process. 20:3 (2012), 806–817.
79. Helander, E., Virtanen, T., Nurminen, J., Gabbouj, M., Voice conversion using partial least squares regression. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 912–921.
80. Helander, E.E., Nurminen, J., A novel method for prosody prediction in voice conversion. Proceedings of the ICASSP, 2007.
81. Helander, E.E., Nurminen, J., On the importance of pure prosody in the perception of speaker identity. Proceedings of the INTERSPEECH, 2007.
82. Hironori, D., Nakamura, K., Tomoki, T., Saruwatari, H., Shikano, K., Esophageal speech enhancement based on statistical voice conversion with gaussian mixture models. IEICE Trans. Inf. Syst. 93:9 (2010), 2472–2482.
83. Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw. 2:5 (1989), 359–366.
84. Hsia, C.-C., Wu, C.-H., Liu, T.-H., Duration-embedded bi-HMM for expressive voice conversion. Proceedings of the INTERSPEECH, 2005.
85. Hsia, C.-C., Wu, C.-H., Wu, J.-Q., Conversion function clustering and selection using linguistic and spectral information for emotional voice conversion. IEEE Trans. Comput. 56:9 (2007), 1245–1254.
86. Huang, D.-Y., Xie, L., Siu, Y., Lee, W., Wu, J., Ming, H., Tian, X., Zhang, S., Ding, C., Li, M., Nguyen, Q.H., Dong, M., Li, H., An automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity. Proceedings of the SSW, 2016.
87. Hwang, H.-T., Tsao, Y., Wang, H.-M., Wang, Y.-R., Chen, S.-H., Incorporating global variance in the training phase of GMM-based voice conversion. Proceedings of the APSIPA, 2013.
88. Hwang, H.-T., Tsao, Y., Wang, H.-M., Wang, Y.-R., Chen, S.-H., et al. A study of mutual information for GMM-based spectral conversion. Proceedings of the INTERSPEECH, 2012.
89. Imai, S., Cepstral analysis synthesis on the mel frequency scale. Proceedings of the ICASSP, 1983.
90. Imai, S., Kobayashi, T., Tokuda, K., Masuko, T., Koishida, K., Sako, S., Zen, H., Speech signal processing toolkit (SPTK), version 3.3. 2009.
91. Imai, S., Sumita, K., Furuichi, C., Mel log spectrum approximation (MLSA) filter for speech synthesis. Electron. Commun. Japan 66:2 (1983), 10–18.
92. Inanoglu, Z., Transforming Pitch in a Voice Conversion Framework, 2003, St. Edmunds College, University of Cambridge Master's Thesis.
93. Inanoglu, Z., Young, S., A system for transforming the emotion in speech: combining data-driven conversion techniques for prosody and voice quality. Proceedings of the INTERSPEECH, 2007, 490–493.
94. Inanoglu, Z., Young, S., Data-driven emotion conversion in spoken english. Speech Commun. 51:3 (2009), 268–283.
95. Iwahashi, N., Sagisaka, Y., Speech spectrum transformation by speaker interpolation. Proceedings of the ICASSP. Vol. 1, 1994, IEEE, I–461.
96. Iwahashi, N., Sagisaka, Y., Speech spectrum conversion based on speaker interpolation and multi-functional representation with weighting by radial basis function networks. Speech Commun. 16:2 (1995), 139–151.
97. Kain, A., Macon, M.W., Spectral voice conversion for text-to-speech synthesis. Proceedings of the ICASSP, 1998.
98. Kain, A., Macon, M.W., Text-to-speech voice adaptation from sparse training data. Proceedings of the ICSLP, 1998.
99. Kain, A., Macon, M.W., Design and evaluation of a voice conversion algorithm based on spectral envelope mapping and residual prediction. Proceedings of the ICASSP, 2001.
100. Kain, A., van Santen, J.P., Unit-selection text-to-speech synthesis using an asynchronous interpolation model. Proceedings of the SSW, 2007.
101. Kain, A., Van Santen, J., Using speech transformation to increase speech intelligibility for the hearing-and speaking-impaired. Proceedings of the ICASSP, 2009.
102. Kain, A.B., High Resolution Voice Transformation, 2001, Oregon Health & Science University Ph.D. thesis.
103. Kain, A.B., Hosom, J.-P., Niu, X., van Santen, J.P., Fried-Oken, M., Staehely, J., Improving the intelligibility of dysarthric speech. Speech Commun. 49:9 (2007), 743–759.
104. Kang, Y., Shuang, Z., Tao, J., Zhang, W., Xu, B., A hybrid gmm and codebook mapping method for spectral conversion. Affective Computing and Intelligent Interaction, 2005, Springer, 303–310.
105. Kang, Y., Tao, J., Xu, B., Applying pitch target model to convert f0 contour for expressive mandarin speech synthesis. Proceedings of the ICASSP, 2006.
106. Kawahara, H., Masuda-Katsuse, I., De Cheveigné, A., Restructuring speech representations using a pitch-adaptive time–frequency smoothing and an instantaneous-frequency-based f0 extraction: Possible role of a repetitive structure in sounds. Speech Commun. 27:3 (1999), 187–207.
107. Kawahara, H., Morise, M., Takahashi, T., Nisimura, R., Irino, T., Banno, H., TANDEM-STRAIGHT: a temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, f0, and aperiodicity estimation. Proceedings of the ICASSP, 2008.
108. Kawanami, H., Iwami, Y., Toda, T., Saruwatari, H., Shikano, K., GMM-based voice conversion applied to emotional speech synthesis. Proceedings of the EUROSPEECH, 2003.
109. Kim, E.-K., Lee, S., Oh, Y.-H., Hidden markov model based voice conversion using dynamic characteristics of speaker. Proceedings of the EUROSPEECH, 1997.
110. Kobayashi, K., Doi, H., Toda, T., Nakano, T., Goto, M., Neubig, G., Sakti, S., Nakamura, S., An investigation of acoustic features for singing voice conversion based on perceptual age. Proceedings of the INTERSPEECH, 2013.
111. Kobayashi, K., Toda, T., Neubig, G., Sakti, S., Nakamura, S., Statistical singing voice conversion based on direct waveform modification with global variance. Proceedings of the INTERSPEECH, 2015.
112. Kominek, J., Black, A.W., The CMU arctic speech databases. Proceedings of the SSW, 2004.
113. Koutsogiannaki, M., Stylianou, Y., Simple and artefact-free spectral modifications for enhancing the intelligibility of casual speech. Proceedings of the ICASSP, 2014.
114. Kumar, A., Verma, A., Using phone and diphone based acoustic models for voice conversion: a step towards creating voice fonts. Proceedings of the ICME, 2003.
115. Kuwabara, H., Sagisak, Y., Acoustic characteristics of speaker individuality: control and conversion. Speech Commun. 16:2 (1995), 165–173.
116. Langarani, M.S.E., van Santen, J., Speaker intonation adaptation for transforming text-to-speech synthesis speaker identity. Proceedings of the ASRU, 2015.
117. Laskar, R., Chakrabarty, D., Talukdar, F., Rao, K.S., Banerjee, K., Comparing ANN and GMM in a voice conversion framework. Appl. Soft Comput. 12:11 (2012), 3332–3342.
118. Laskar, R.H., Talukdar, F.A., Bhattacharjee, R., Das, S., Voice conversion by mapping the spectral and prosodic features using support vector machine. Applications of Soft Computing, 2009, Springer, 519–528.
119. Latorre, J., Wan, V., Yanagisawa, K., Voice expression conversion with factorised HMM-TTS models. Proceedings of the INTERSPEECH, 2014.
120. Lee, C.-H., Wu, C.-H., Map-based adaptation for speech conversion using adaptation data selection and non-parallel training. Proceedings of the INTERSPEECH, 2006.
121. Lee, K.-S., Statistical approach for voice personality transformation. IEEE Trans. Audio Speech Lang. Process. 15:2 (2007), 641–651.
122. Lee, K.-S., A unit selection approach for voice transformation. Speech Commun. 60 (2014), 30–43.
123. Li, B., Xiao, Z., Shen, Y., Zhou, Q., Tao, Z., Emotional speech conversion based on spectrum-prosody dual transformation. Proceedings of the ICSP, 2012.
124. Ling, Z.-H., Kang, S.-Y., Zen, H., Senior, A., Schuster, M., Qian, X.-J., Meng, H.M., Deng, L., Deep learning for acoustic modeling in parametric speech generation: A systematic review of existing techniques and future trends. Signal Process. Mag. IEEE 32:3 (2015), 35–52.
125. Liu, L.-J., Chen, L.-H., Ling, Z.-H., Dai, L.-R., Using bidirectional associative memories for joint spectral envelope modeling in voice conversion. Proceedings of the ICASSP, 2014.
126. Liu, L.-J., Chen, L.-H., Ling, Z.-H., Dai, L.-R., Spectral conversion using deep neural networks trained with multi-source speakers. Proceedings of the ICASSP, 2015.
127. Lolive, D., Barbot, N., Boeffard, O., Pitch and duration transformation with non-parallel data. Proceedings of the Speech Prosody, 2008.
128. Machado, A.F., Queiroz, M., Voice conversion: a critical survey. Proceedings of the SMC, 2010.
129. Maeda, N., Banno, H., Kajita, S., Takeda, K., Itakura, F., Speaker conversion through non-linear frequency warping of straight spectrum. Proceedings of the EUROSPEECH, 1999.
130. Makki, B., Seyedsalehi, S., Sadati, N., Hosseini, M.N., Voice conversion using nonlinear principal component analysis. Proceedings of the CIISP, 2007.
131. Masaka, K., Aihara, R., Takiguchi, T., Ariki, Y., Multimodal voice conversion using non-negative matrix factorization in noisy environments. Proceedings of the ICASSP, 2014.
132. Masuda, T., Shozakai, M., Cost reduction of training mapping function based on multistep voice conversion. Proceedings of the ICASSP, 2007.
133. Matsumoto, H., Hiki, S., Sone, T., Nimura, T., Multidimensional representation of personal quality of vowels and its acoustical correlates. IEEE Trans. Audio Electroacoust. 21:5 (1973), 428–436.
134. Matsumoto, H., Yamashita, Y., Unsupervised speaker adaptation from short utterances based on a minimized fuzzy objective function. J. Acoust. Soc. Japan (E) 14:5 (1993), 353–361.
135. Mesbahi, L., Barreaud, V., Boeffard, O., Comparing GMM-based speech transformation systems. Proceedings of the INTERSPEECH, 2007.
136. Mesbahi, L., Barreaud, V., Boeffard, O., Gmm-based speech transformation systems under data reduction. Proceedings of the SSW, 2007.
137. Ming, H., Huang, D., Xie, L., Wu, J., Li, M.D.H., Deep bidirectional lstm modeling of timbre and prosody for emotional voice conversion. Proceedings of the INTERSPEECH, 2016.
138. Mizuno, H., Abe, M., Voice conversion algorithm based on piecewise linear conversion rules of formant frequency and spectrum tilt. Speech Commun. 16:2 (1995), 153–164.
139. Mohammadi, S.H., Kain, A., Transmutative voice conversion. Proceedings of the ICASSP, 2013.
140. Mohammadi, S.H., Kain, A., Voice conversion using deep neural networks with speaker-independent pre-training. Proceedings of the SLT, 2014.
141. Mohammadi, S.H., Kain, A., Semi-supervised training of a voice conversion mapping function using a joint-autoencoder. Proceedings of the INTERSPEECH, 2015.
142. Mohammadi, S.H., Kain, A., A voice conversion mapping function based on a stacked joint-autoencoder. Proceedings of the INTERSPEECH, 2016.
143. Mohammadi, S.H., Kain, A., van Santen, J.P., Making conversational vowels more clear. Proceedings of the INTERSPEECH, 2012.
144. Morise, M., Cheaptrick, a spectral envelope estimator for high-quality speech synthesis. Speech Commun. 67 (2015), 1–7.
145. Morise, M., Yokomori, F., Ozawa, K., World: a vocoder-based high-quality speech synthesis system for real-time applications. IEICE Trans. Inf. Syst., 2016.
146. Morley, E., Klabbers, E., van Santen, J.P., Kain, A., Mohammadi, S.H., Synthetic f0 can effectively convey speaker id in delexicalized speech. Proceedings of the INTERSPEECH, 2012.
147. Morris, R.W., Clements, M.A., Reconstruction of speech from whispers. Med. Eng. Phys. 24:7 (2002), 515–520.
148. Mouchtaris, A., Agiomyrgiannakis, Y., Stylianou, Y., Conditional vector quantization for voice conversion. Proceedings of the ICASSP, 2007.
149. Mouchtaris, A., Van der Spiegel, J., Mueller, P., Non-parallel training for voice conversion by maximum likelihood constrained adaptation. Proceedings of the ICASSP, 2004.
150. Mouchtaris, A., Van der Spiegel, J., Mueller, P., A spectral conversion approach to the iterative wiener filter for speech enhancement. Proceedings of the ICME, 2004.
151. Mouchtaris, A., Van der Spiegel, J., Mueller, P., Nonparallel training for voice conversion based on a parameter adaptation approach. IEEE Trans. Audio Speech Lang. Process. 14:3 (2006), 952–963.
152. Moulines, E., Charpentier, F., Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones. Speech Commun. 9:5 (1990), 453–467.
153. Muramatsu, T., Ohtani, Y., Toda, T., Saruwatari, H., Shikano, K., Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory. Proceedings of the INTERSPEECH, 2008.
154. Nakagiri, M., Toda, T., Kashioka, H., Shikano, K., Improving body transmitted unvoiced speech with statistical voice conversion. Proceedings of the INTERSPEECH, 2006.
155. Nakamura, K., Toda, T., Saruwatari, H., Shikano, K., A speech communication aid system for total laryngectomies using voice conversion of body transmitted artificial speech. J. Acoust. Soc. Am., 120(5), 2006, 3351.
156. Nakamura, K., Toda, T., Saruwatari, H., Shikano, K., Speaking-aid systems using GMM -based voice conversion for electrolaryngeal speech. Speech Commun. 54:1 (2012), 134–146.
157. Nakashika, T., Takashima, R., Takiguchi, T., Ariki, Y., Voice conversion in high-order eigen space using deep belief nets. Proceedings of the INTERSPEECH, 2013.
158. Nakashika, T., Takiguchi, T., Ariki, Y., High-order sequence modeling using speaker-dependent recurrent temporal restricted boltzmann machines for voice conversion. Proceedings of the INTERSPEECH, 2014.
159. Nakashika, T., Takiguchi, T., Ariki, Y., Sparse nonlinear representation for voice conversion. Proceedings of the ICME, 2015.
160. Nakashika, T., Takiguchi, T., Ariki, Y., Voice conversion using RNN pre-trained by recurrent temporal restricted Boltzmann machines. IEEE/ACM Trans. Audio Speech Lang. Process. 23:3 (2015), 580–587, 10.1109/TASLP.2014.2379589.
161. Nakashika, T., Takiguchi, T., Ariki, Y., Voice conversion using speaker-dependent conditional restricted Boltzmann machine. EURASIP J. Audio Speech Music Process. 2015:1 (2015), 1–12.
162. Nakashika, T., Takiguchi, T., Minami, Y., Non-parallel training in voice conversion using an adaptive restricted boltzmann machine. IEEE/ACM Trans. Audio Speech Lang. Process. 24:11 (2016), 2032–2045.
163. Nakashika, T., Toru, Takiguchi, T., Tetsuya, Ariki, Y., Yasuo, Voice conversion based on speaker-dependent restricted boltzmann machines. IEICE Trans. Inf. Syst. 97:6 (2014), 1403–1410.
164. Nankaku, Y., Nakamura, K., Toda, T., Tokuda, K., Spectral conversion based on statistical models including time-sequence matching. Proceedings of the SSW, 2007.
165. Narendranath, M., Murthy, H.A., Rajendran, S., Yegnanarayana, B., Transformation of formants for voice conversion using artificial neural networks. Speech Commun. 16:2 (1995), 207–216.
166. Nguyen, B.P., Studies on Spectral Modification in Voice Transformation, 2009, Japan Advanced Institute of Science and Technology Ph.D. thesis.
167. Nguyen, B.P., Akagi, M., Spectral modification for voice gender conversion using temporal decomposition. J.Signal Process, 2007.
168. Nguyen, B.P., Akagi, M., Phoneme-based spectral voice conversion using temporal decomposition and gaussian mixture model. Proceedings of the ICCE, 2008.
169. Nirmal, J., Patnaik, S., Zaveri, M.A., Voice transformation using radial basis function. Proceedings of the TITC, 2013, Springer, 345–351.
170. Nirmal, J., Zaveri, M., Patnaik, S., Kachare, P., Voice conversion using general regression neural network. Appl. Soft Comput. 24 (2014), 1–12.
171. Nurminen, J., Popa, V., Tian, J., Tang, Y., Kiss, I., A parametric approach for voice conversion. TCSTAR WSST, 2006, 225–229.
172. Nurminen, J., Tian, J., Popa, V., Voicing level control with application in voice conversion. Proceedings of the INTERSPEECH, 2007.
173. Ohtani, Y., Techniques for Improving Voice Conversion Based on Eigenvoices, 2010, Nara Institute of Science and Technology.
174. Ohtani, Y., Toda, T., Saruwatari, H., Shikano, K., Maximum likelihood voice conversion based on GMM with STRAIGHT mixed excitation. Proceedings of the INTERSPEECH, 2006.
175. Ohtani, Y., Toda, T., Saruwatari, H., Shikano, K., Many-to-many eigenvoice conversion with reference voice. Proceedings of the INTERSPEECH, 2009.
176. Ohtani, Y., Toda, T., Saruwatari, H., Shikano, K., Non-parallel training for many-to-many eigenvoice conversion. Proceedings of the ICASSP, 2010.
177. Paliwal, K.K., Interpolation properties of linear prediction parametric representations. Proceedings of the EUROSPEECH, 1995.
178. Park, K.-Y., Kim, H.S., Narrowband to wideband conversion of speech using gmm based transformation. Proceedings of the ICASSP, 2000.
179. Patterson, D.J., linguistic Approach to Pitch Range Modelling, 2000, Edinburgh University Ph.D. thesis.
180. Pellom, B.L., Hansen, J.H., An experimental study of speaker verification sensitivity to computer voice-altered imposters. Proceedings of the ICASSP, 1999.
181. Percybrooks, W.S., Moore, E., Voice conversion with linear prediction residual estimaton. Proceedings of the ICASSP, 2008.
182. Pilkington, N.C., Zen, H., Gales, M.J., et al. Gaussian process experts for voice conversion. Proceedings of the INTERSPEECH, 2011.
183. Pitz, M., Ney, H., Vocal tract normalization equals linear transformation in cepstral space. Speech Audio Process. IEEE Trans. 13:5 (2005), 930–944.
184. Pongkittiphan, T., Eigenvoice-Based Character Conversion and its Evaluations, 2012, The University of Tokyo Master's thesis.
185. Popa, V., Nurminen, J., Gabbouj, M., A novel technique for voice conversion based on style and content decomposition with bilinear models. Proceedings of the INTERSPEECH, 2009.
186. Popa, V., Nurminen, J., Gabbouj, M., et al. A study of bilinear models in voice conversion. J. Signal Inf. Process., 2(02), 2011, 125.
187. Popa, V., Silen, H., Nurminen, J., Gabbouj, M., Local linear transformation for voice conversion. Proceedings of the ICASSP, 2012.
188. Pozo, A., Voice Source and Duration Modelling for Voice Conversion and Speech Repair, 2008, University of Cambridge Ph.D. thesis.
189. Přibilová, A., Přibil, J., Non-linear frequency scale mapping for voice conversion in text-to-speech system with cepstral description. Speech Commun. 48:12 (2006), 1691–1703.
190. Qiao, Y., Tong, T., Minematsu, N., A study on bag of gaussian model with application to voice conversion. Proceedings of the INTERSPEECH, 2011, 657–660.
191. Ramos, M.V., Voice Conversion with Deep Learning, 2016, Tecnico Lisboa Master's thesis.
192. Rao, K.S., Laskar, R., Koolagudi, S.G., Voice transformation by mapping the features at syllable level. Pattern Recognition and Machine Intelligence, 2007, Springer, 479–486.
193. Rao, S.V., Shah, N.J., Patil, H.A., Novel pre-processing using outlier removal in voice conversion. Proceedings of the SSW, 2016.
194. Rentzos, D., Qin, S.V., Ho, C.-H., Turajlic, E., Probability models of formant parameters for voice conversion. Proceedings of the EUROSPEECH, 2003.
195. Rinscheid, A., Voice conversion based on topological feature maps and time-variant filtering. Proceedings of the ICSLP, 1996.
196. Rix, A.W., Beerends, J.G., Hollier, M.P., Hekstra, A.P., Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. Proceedings of the ICASSP, 2001.
197. Saito, D., Watanabe, S., Nakamura, A., Minematsu, N., Statistical voice conversion based on noisy channel model. IEEE Trans. Audio Speech Lang. Process. 20:6 (2012), 1784–1794.
198. Saito, D., Yamamoto, K., Minematsu, N., Hirose, K., One-to-many voice conversion based on tensor representation of speaker space. Proceedings of the INTERSPEECH, 2011.
199. Salor, Ö., Demirekler, M., Dynamic programming approach to voice transformation. Speech communication 48:10 (2006), 1262–1272.
200. Sanchez, G., Silen, H., Nurminen, J., Gabbouj, M., Hierarchical modeling of f0 contours for voice conversion. Proceedings of the INTERSPEECH, 2014.
201. Shikano, K., Nakamura, S., Abe, M., Speaker adaptation and voice conversion by codebook mapping. IEEE International Sympoisum on Circuits and Systems, 1991, 594–597.
202. Shuang, Z., Bakis, R., Qin, Y., Voice conversion based on mapping formants. TC-STAR WSST, 2006, 219–223.
203. Shuang, Z., Meng, F., Qin, Y., Voice conversion by combining frequency warping with unit selection. Proceedings of the ICASSP, 2008.
204. Shuang, Z.-W., Wang, Z.-X., Ling, Z.-H., Wang, R.-H., A novel voice conversion system based on codebook mapping with phoneme-tied weighting. Proceedings of the ICSLP, 2004.
205. Song, P., Bao, Y., Zhao, L., Zou, C., Voice conversion using support vector regression. Electron. Lett. 47:18 (2011), 1045–1046.
206. Sorin, A., Shechtman, S., Pollet, V., Uniform speech parameterization for multi-form segment synthesis. Proceedings of the INTERSPEECH, 2011.
207. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1 (2014), 1929–1958.
208. Stylianou, I., Harmonic Plus Noise Models for Speech, Combined with Statistical Methods, for Speech and Speaker Modification, 1996, Ecole Nationale Supérieure des Télécommunications Ph.D. thesis.
209. Stylianou, Y., Voice transformation: a survey. Proceedings of the ICASSP, 2009.
210. Stylianou, Y., Cappé, O., Moulines, E., Continuous probabilistic transform for voice conversion. IEEE Trans. Speech Audio Process. 6:2 (1998), 131–142.
211. Sun, L., Kang, S., Li, K., Meng, H., Voice conversion using deep bidirectional long short-term memory based recurrent neural networks. Proceedings of the ICASSP, 2015.
212. Sündermann, D., Voice conversion: State-of-the-art and future work. Fortschritte der Akustik, 31(2), 2005, 735.
213. Sündermann, D., Text-independent voice conversion, 2008, Universitätsbibliothek der Universität der Bundeswehr München Ph.D. thesis.
214. Sündermann, D., Bonafonte, A., Höge, H., Ney, H., Voice conversion using exclusively unaligned training data. Proceedings of the ACL/SEPLN, 2004.
215. Sündermann, D., Bonafonte, A., Ney, H., Höge, H., A first step towards text-independent voice conversion. Proceedings of the ICSLP, 2004.
216. Sündermann, D., Bonafonte, A., Ney, H., Höge, H., A study on residual prediction techniques for voice conversion. Proceedings of the ICASSP, 2005.
217. Sündermann, D., Hoge, H., Bonafonte, A., Ney, H., Black, A., Narayanan, S., Text-independent voice conversion based on unit selection. Proceedings of the ICASSP, 2006.
218. Sündermann, D., Höge, H., Bonafonte, A., Ney, H., Hirschberg, J., TC-Star: cross-language voice conversion revisited. TC-Star Workshop, 2006, TC-Star Workshop.
219. Sündermann, D., Höge, H., Bonafonte, A., Ney, H., Hirschberg, J., Text-independent cross-language voice conversion. Proceedings of the INTERSPEECH, 2006.
220. Sündermann, D., Ney, H., An automatic segmentation and mapping approach for voice conversion parameter training. Proceedings of the AST, 2003.
221. Sündermann, D., Ney, H., Hoge, H., VTLN-based cross-language voice conversion. Proceedings of the ASRU, 2003.
222. Suni, A.S., Aalto, D., Raitio, T., Alku, P., Vainio, M., et al. Wavelets for intonation modeling in hmm speech synthesis. Proceedings of the SSW, 2013.
223. Takamichi, S., Toda, T., Black, A.W., Nakamura, S., Modulation spectrum-based post-filter for GMM-based voice conversion. Proceedings of the APSIPA, 2014.
224. Takamichi, S., Toda, T., Black, A.W., Nakamura, S., Modulation spectrum-constrained trajectory training algorithm for gmm-based voice conversion. Proceedings of the ICASSP, 2015.
225. Takashima, R., Aihara, R., Takiguchi, T., Ariki, Y., Noise-robust voice conversion based on spectral mapping on sparse space. Proceedings of the SSW, 2013.
226. Takashima, R., Takiguchi, T., Ariki, Y., Exemplar-based voice conversion in noisy environment. Proceedings of the SLT, 2012.
227. Tamura, M., Morita, M., Kagoshima, T., Akamine, M., One sentence voice adaptation using GMm-based frequency-warping and shift with a sub-band basis spectrum model. Proceedings of the ICASSP, 2011.
228. Tanaka, K., Toda, T., Neubig, G., Sakti, S., Nakamura, S., A hybrid approach to electrolaryngeal speech enhancement based on spectral subtraction and statistical voice conversion. Proceedings of the INTERSPEECH, 2013.
229. Tani, D., Toda, T., Ohtani, Y., Saruwatari, H., Shikano, K., Maximum a posteriori adaptation for many-to-one eigenvoice conversion. Proceedings of the INTERSPEECH, 2008.
230. Tao, J., Kang, Y., Li, A., Prosody conversion from neutral speech to emotional speech. IEEE Trans. Audio Speech Lang. Process. 14:4 (2006), 1145–1154.
231. Tao, J., Zhang, M., Nurminen, J., Tian, J., Wang, X., Supervisory data alignment for text-independent voice conversion. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 932–943.
232. Tesser, F., Zovato, E., Nicolao, M., Cosi, P., Two vocoder techniques for neutral to emotional timbre conversion. Proceedings of the SSW, 2010.
233. Tian, X., Wu, Z., Lee, S., Chng, E.S., Correlation-based frequency warping for voice conversion. Proceedings of the ISCSLP, 2014, IEEE, 211–215.
234. Tian, X., Wu, Z., Lee, S.W., Hy, N.Q., Chng, E.S., Dong, M., Sparse representation for frequency warping based voice conversion. Proceedings of the ICASSP, 2015.
235. Tian, X., Wu, Z., Lee, S.W., Hy, N.Q., Dong, M., Chng, E.S., System fusion for high-performance voice conversion. Proceedings of the INTERSPEECH, 2015.
236. Titterington, D.M., Smith, A.F., Makov, U.E., et al. Statistical Analysis of Finite Mixture Distributions. Vol. 7, 1985, Wiley New York.
237. Toda, T., Black, A.W., Tokuda, K., Acoustic-to-articulatory inversion mapping with gaussian mixture model. Proceedings of the INTERSPEECH, 2004.
238. Toda, T., Black, A.W., Tokuda, K., Spectral conversion based on maximum likelihood estimation considering global variance of converted parameter. Proceedings of the ICASSP, 2005.
239. Toda, T., Black, A.W., Tokuda, K., Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory. IEEE Trans. Audio Speech Lang. Process. 15:8 (2007), 2222–2235.
240. Toda, T., Black, A.W., Tokuda, K., Statistical mapping between articulatory movements and acoustic spectrum using a gaussian mixture model. Speech Commun. 50:3 (2008), 215–227.
241. Toda, T., Muramatsu, T., Banno, H., Implementation of computationally efficient real-time voice conversion. Proceedings of the INTERSPEECH, 2012.
242. Toda, T., Nakagiri, M., Shikano, K., Statistical voice conversion techniques for body-conducted unvoiced speech enhancement. IEEE Trans. Audio Speech Lang. Process. 20:9 (2012), 2505–2517.
243. Toda, T., Nakamura, K., Saruwatari, H., Shikano, K., et al. Alaryngeal speech enhancement based on one-to-many eigenvoice conversion. IEEE/ACM IEEE Trans. Audio Speech Lang. Process. 22:1 (2014), 172–183.
244. Toda, T., Ohtani, Y., Shikano, K., Eigenvoice conversion based on gaussian mixture model. Proceedings of the INTERSPEECH, 2006.
245. Toda, T., Ohtani, Y., Shikano, K., One-to-many and many-to-one voice conversion based on eigenvoices. Proceedings of the ICASSP, 2007.
246. Toda, T., Saito, D., Villavicencio, F., Yamagishi, J., Wester, M., Wu, Z., Chen, L.-H., et al. The voice conversion challenge 2016. Proceedings of the INTERSPEECH, 2016.
247. Toda, T., Saruwatari, H., Shikano, K., Voice conversion algorithm based on gaussian mixture model with dynamic frequency warping of straight spectrum. Proceedings of the ICASSP, 2001.
248. Toda, T., Shikano, K., NAM-to-speech conversion with gaussian mixture models. Proceedings of the INTERSPEECH, 2005.
249. Tokuda, K., Kobayashi, T., Imai, S., Speech parameter generation from HMM using dynamic features. Proceedings of the ICASSP, 1995.
250. Tokuda, K., Zen, H., Directly modeling speech waveforms by neural networks for statistical parametric speech synthesis. Proceedings of the ICASSP, 2015.
251. Tran, V.-A., Bailly, G., Lœvenbruck, H., Toda, T., Improvement to a nam-captured whisper-to-speech system. Speech Commun. 52:4 (2010), 314–326.
252. Turajlic, E., Rentzos, D., Vaseghi, S., Ho, C.-H., Evaluation of methods for parameteric formant transformation in voice conversion. Proceeding of the ICASSP, 2003.
253. Türk, O., Cross-Lingual Voice Conversion, 2007, Bogaziçi University Ph.D. thesis.
254. Türk, O., Arslan, L.M., Voice conversion methods for vocal tract and pitch contour modification. Proceedings of the INTERSPEECH, 2003.
255. Turk, O., Arslan, L.M., Donor selection for voice conversion. Proceedings of the EUSIPCO, 2005.
256. Turk, O., Arslan, L.M., Robust processing techniques for voice conversion. Comput. Speech Lang. 20:4 (2006), 441–467.
257. Turk, O., Buyuk, O., Haznedaroglu, A., Arslan, L.M., Application of voice conversion for cross-language rap singing transformation. Proceedings of the ICASSP, 2009.
258. Türk, O., Schröder, M., A comparison of voice conversion methods for transforming voice quality in emotional speech synthesis. Proceedings of the INTERSPEECH, 2008.
259. Turk, O., Schroder, M., Evaluation of expressive speech synthesis with voice conversion and copy resynthesis techniques. IEEE Trans. Audio Speech Lang. Process. 18:5 (2010), 965–973.
260. Uchino, E., Yano, K., Azetsu, T., A self-organizing map with twin units capable of describing a nonlinear input–output relation applied to speech code vector mapping. Inf. Sci. 177:21 (2007), 4634–4644.
261. Uriz, A., Aguero, P., Tulli, J., Gonzalez, E., Bonafonte, A., Voice conversion using frame selection and warping functions. Proceedings of the RPIC, 2009.
262. Uriz, A., Agüero, P.D., Erro, D., Bonafonte, A., Voice Conversion Using Frame Selection. 2008 Reporte Interno Laboratorio de Comunicaciones-UNMdP.
263. Uriz, A.J., Agüero, P.D., Bonafonte, A., Tulli, J.C., Voice conversion using k-histograms and frame selection. Proceedings of the INTERSPEECH, 2009.
264. Uto, Y., Nankaku, Y., Toda, T., Lee, A., Tokuda, K., Voice conversion based on mixtures of factor analyzers. Proceeding of the ICSLP, 2006.
265. Valbret, H., Moulines, E., Tubach, J.-P., Voice transformation using PSOLA technique. Proceedings of the ICASSP, 1992.
266. Valbret, H., Moulines, E., Tubach, J.P., Voice transformation using PSOLA technique. Speech Commun. 11:2 (1992), 175–187.
267. Valentini-Botinhao, C., Wu, Z., King, S., Towards minimum perceptual error training for DNN-based speech synthesis. Proceedings of the INTERSPEECH, 2015.
268. Veaux, C., Rodet, X., Intonation conversion from neutral to expressive speech. Proceedings of the INTERSPEECH, 2011.
269. Verma, A., Kumar, A., Voice fonts for individuality representation and transformation. ACM Trans. Speech Lang. Process. (TSLP), 2(1), 2005, 4.
270. Villavicencio, F., Bonada, J., Applying voice conversion to concatenative singing-voice synthesis. Proceedings of the INTERSPEECH, 2010.
271. Villavicencio, F., Bonada, J., Hisaminato, Y., Observation-model error compensation for enhanced spectral envelope transformation in voice conversion. Proceedings of the MLSP, 2015.
272. Vincent, D., Rosec, O., Chonavel, T., A new method for speech synthesis and transformation based on an arx-lf source-filter decomposition and HNM modeling. Proceedings of the ICASSP, 2007.
273. Wahlster, W., Verbmobil: Foundations of Speech-to-Speech Translation. 2000, Springer Science & Business Media.
274. Wang, M., Wen, M., Hirose, K., Minematsu, N., Emotional voice conversion for mandarin using tone nucleus model–small corpus and high efficiency. Proceedings of the Speech Prosody, 2012.
275. Wang, Z., Yu, Y., Multi-level prosody and spectrum conversion for emotional speech synthesis. Proceedings of the ICSP, 2014.
276. Watanabe, T., Murakami, T., Namba, M., Hoya, T., Ishida, Y., Transformation of spectral envelope for voice conversion based on radial basis function networks. Proceedings of the ICSLP, 2002.
277. Wester, M., Wu, Z., Yamagishi, J., Analysis of the voice conversion challenge 2016 evaluation results. Proceedings of the INTERSPEECH, 2016.
278. Wester, M., Wu, Z., Yamagishi, J., Multidimensional scaling of systems in the voice conversion challenge 2016. Proceedings of the SSW, 2016.
279. Wrench, A., The MOCHA-TIMIT articulatory database. 1999, Queen Margaret University College.
280. Wu, C.-H., Hsia, C.-C., Liu, T.-H., Wang, J.-F., Voice conversion using duration-embedded bi-HMMs for expressive speech synthesis. IEEE Trans. Audio Speech Lang. Process. 14:4 (2006), 1109–1116.
281. Wu, Y.-C., Hwang, H.-T., Hsu, C.-C., Tsao, Y., Wang, H.-M., Locally linear embedding for exemplar-based spectral conversion. Proceedings of the INTERSPEECH, 2016.
282. Wu, Z., Chng, E.S., Li, H., Conditional restricted boltzmann machine for voice conversion. Proceedings of the ChinaSIP, 2013.
283. Wu, Z., Chng, E.S., Li, H., Joint nonnegative matrix factorization for exemplar-based voice conversion. Proceedings of the INTERSPEECH, 2014.
284. Wu, Z., Kinnunen, T., Chng, E., Li, H., Text-independent F0 transformation with non-parallel data for voice conversion. Proceedings of the INTERSPEECH, 2010.
285. Wu, Z., Kinnunen, T., Chng, E.S., Li, H., Mixture of factor analyzers using priors from non-parallel speech for voice conversion. IEEE Signal Process. Lett. 19:12 (2012), 914–917.
286. Wu, Z., Larcher, A., Lee, K.-A., Chng, E., Kinnunen, T., Li, H., Vulnerability evaluation of speaker verification under voice conversion spoofing: the effect of text constraints. Proceedings of the INTERSPEECH, 2013.
287. Wu, Z., Li, H., Voice conversion versus speaker verification: an overview. APSIPA Trans. Signal Inf. Process., 3, 2014, e17.
288. Wu, Z., Virtanen, T., Chng, E.S., Li, H., Exemplar-based sparse representation with residual compensation for voice conversion. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 22:10 (2014), 1506–1521.
289. Wu, Z., Virtanen, T., Kinnunen, T., Chng, E., Li, H., Exemplar-based unit selection for voice conversion utilizing temporal information. Proceedings of the INTERSPEECH, 2013.
290. Wu, Z., Virtanen, T., Kinnunen, T., Chng, E.S., Li, H., Exemplar-based voice conversion using non-negative spectrogram deconvolution. Proceedings of the SSW, 2013.
291. Xie, F.-L., Qian, Y., Fan, Y., Soong, F.K., Li, H., Sequence error (se) minimization training of neural network for voice conversion. Proceedings of the INTERSPEECH, 2014.
292. Xie, F.-L., Qian, Y., Soong, F.K., Li, H., Pitch transformation in neural network based voice conversion. Proceedings of the ISCSLP, 2014.
293. Xu, N., Tang, Y., Bao, J., Jiang, A., Liu, X., Yang, Z., Voice conversion based on gaussian processes by coherent and asymmetric training with limited training data. Speech Commun. 58 (2014), 124–138.
294. Yamagishi, J., Veaux, C., King, S., Renals, S., Speech synthesis technologies for individuals with vocal disabilities: voice banking and reconstruction. Acoust. Sci. Technol. 33:1 (2012), 1–5.
295. Ye, H., Young, S., Perceptually weighted linear transformations for voice conversion. Proceedings of the INTERSPEECH, 2003.
296. Ye, H., Young, S., Voice conversion for unknown speakers. Proceedings of the INTERSPEECH, 2004.
297. Ye, H., Young, S., Quality-enhanced voice morphing using maximum likelihood transformations. IEEE Trans. Audio Speech Lang. Process. 14:4 (2006), 1301–1312.
298. Yue, Z., Zou, X., Jia, Y., Wang, H., Voice conversion using HMM combined with GMM. Proceedings of the CISP, 2008.
299. Yutani, K., Uto, Y., Nankaku, Y., Lee, A., Tokuda, K., Voice conversion based on simultaneous modelling of spectrum and f0. Proceedings of the ICASSP, 2009.
300. Zen, H., Nankaku, Y., Tokuda, K., Continuous stochastic feature mapping based on trajectory hmms. IEEE Trans. Audio Speech Lang. Process. 19:2 (2011), 417–430.
301. Zhang, J., Sun, J., Dai, B., Voice conversion based on weighted least squares estimation criterion and residual prediction from pitch contour. Affective Computing and Intelligent Interaction, 2005, Springer, 326–333.
302. Zhang, M., Tao, J., Nurminen, J., Tian, J., Wang, X., Phoneme cluster based state mapping for text-independent voice conversion. Proceedings of the ICASSP, 2009.
303. Zhang, M., Tao, J., Tian, J., Wang, X., Text-independent voice conversion based on state mapped codebook. Proceedings of the ICASSP, 2008.
304. Zolfaghari, P., Robinson, T., A formant vocoder based on mixtures of gaussians. Proceedings of the ICASSP, 1997.
305. Zorilă, T.-C., Erro, D., Hernaez, I., Improving the quality of standard GMM-based voice conversion systems by considering physically motivated linear transformations. Advances in Speech and Language Technologies for Iberian Languages, 2012, Springer, 30–39.