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Text-to-speech synthesizer based on combination of composite wavelet and hidden Markov models
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In: http://www.brl.ntt.co.jp/people/kameoka/publications/Hojo2013SSW08published.pdf (2013)
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One-to-many voice conversion based on tensor representation of speaker space
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2011/INTERSPEECH_p653-656_t2011-8.pdf (2011)
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Probabilistic integration of joint density model and speaker model for voice conversion
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2010/INTERSPEECH_p1728-1731_t2010-9.pdf (2010)
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Optimal event search using a structural cost function –improvement of structure to speech conversion
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2009/INTERSPEECH_p2047-2050_t2009-9.pdf (2009)
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Gesture Design of Hand-to-Speech Converter Derived from Speech-to-Hand Converter Based on Probabilistic Integration Model
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2011/INTERSPEECH_p3025-3028_t2011-8.pdf
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Experimental Study of Structure to Speech Conversion-- An implementation of Infant-like Vocal Imitation on a Machine--
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2008/ICSP_p651-654_t2008-10.pdf
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Effects of Speaker Adaptive Training on Tensor-based Arbitrary Speaker Conversion
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2012/INTERSPEECH_1274_t2012-9.pdf
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Abstract:
This paper introduces speaker adaptive training techniques to tensor-based arbitrary speaker conversion. In voice conversion studies, realization of conversion from/to an arbitrary speaker’s voice is one of the important objectives. For this purpose, eigen-voice conversion (EVC), which is based on an eigenvoice Gaus-sian mixture model (EV-GMM), was proposed. Although the EVC can effectively construct the conversion model for arbi-trary target speakers using only a few utterances, increase of the utterances used to construct the conversion model does not always improve the conversion performance. This is because the EV-GMMmethod has an inherent problem in representation of GMM supervectors. We previously proposed tensor-based speaker space as a solution for this problem, and realized more flexible control of speaker characteristics. In this paper, to aim larger improvement of the performance of VC, speaker adaptive training and tensor-based speaker representation are integrated. The proposed method can construct the flexible and precise con-version model, and experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed ap-proach. Index Terms: voice conversion, Gaussian mixture model, eigenvoice, Tucker decomposition, speaker adaptive training
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.588.7600
http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2012/INTERSPEECH_1274_t2012-9.pdf
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DIRECTIONAL DEPENDENCY OF CEPSTRUM ON VOCAL TRACT LENGTH
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2008/ICASSP_p4485-4488_t2008-4.pdf
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HMM-BASED SEQUENCE-TO-FRAME MAPPING FOR VOICE CONVERSION
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2010/ICASSP_p4830-4833_t2010-3.pdf
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| 10 |
Improvement of Structure to Speech Conversion Using Iterative Optimization
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In: http://www.gavo.t.u-tokyo.ac.jp/%7Emine/paper/PDF/2009/SPECOM_p174-179_t2009-6.pdf
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Improved Generation of Speech from Its Abstract and Structural Representation
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In: http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2010/ICSP_p597-600_t2010-11.pdf
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