Review: Statistical parametric speech synthesis
Speech Communication
Silent-speech enhancement using body-conducted vocal-tract resonance signals
Speech Communication
Voice conversion using partial least squares regression
IEEE Transactions on Audio, Speech, and Language Processing
Voice conversion based on weighted frequency warping
IEEE Transactions on Audio, Speech, and Language Processing
Supervisory data alignment for text-independent voice conversion
IEEE Transactions on Audio, Speech, and Language Processing
Spectral mapping using artificial neural networks for voice conversion
IEEE Transactions on Audio, Speech, and Language Processing
Synthesis of child speech with HMM adaptation and voice conversion
IEEE Transactions on Audio, Speech, and Language Processing
Hierarchical prosody conversion using regression-based clustering for emotional speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech
Speech Communication
A voice conversion method using segmental GMMs and automatic GMM selection
ROCLING '11 ROCLING 2011 Poster Papers
International Journal of Speech Technology
Voice conversion using linear prediction coefficients and artificial neural network
Proceedings of the CUBE International Information Technology Conference
Alaryngeal Speech Enhancement Based on One-to-Many Eigenvoice Conversion
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters frame by frame based on the minimum mean square error. Although it is reasonably effective, the deterioration of speech quality is caused by some problems: 1) appropriate spectral movements are not always caused by the frame-based conversion process, and 2) the converted spectra are excessively smoothed by statistical modeling. In order to address those problems, we propose a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory. Not only static but also dynamic feature statistics are used for realizing the appropriate converted spectrum sequence. Moreover, the oversmoothing effect is alleviated by considering a global variance feature of the converted spectra. Experimental results indicate that the performance of VC can be dramatically improved by the proposed method in view of both speech quality and conversion accuracy for speaker individuality.