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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.(More)
In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory(More)
This paper describes a novel parameter generation algorithm for the HMM-based speech synthesis. The conventional algorithm generates a trajectory of static features that maximizes an output probability of a parameter sequence consisting of the static and dynamic features from HMMs under an actual constraint between the two features. The generated trajectory(More)
In January 2005, an open evaluation of corpus-based textto-speech synthesis systems using common speech datasets, named Blizzard Challenge 2005, was conducted. Nitech group participated to this challenge with a newly designed HMM-based speech synthesis system (Nitech-HTS 2005). In the present paper, technical details, building processes, and the performance(More)
This paper describes a novel parameter generation algorithm for the HMM-based speech synthesis. The conventional algorithm generates a trajectory of static features that maximizes an output probability of a parameter sequence consisting of the static and dynamic features from HMMs under an actual constraint between the two features. The generated trajectory(More)
This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous(More)
This paper describes a method for determining the vocal tract spectrum from articulatory movements using a Gaussian Mixture Model (GMM) to synthesize speech with articulatory information. The GMM on joint probability density of articulatory parameters and acoustic spectral parameters is trained using a parallel acousticarticulatory speech database. We(More)
In the voice conversion algorithm based on the Gaussian Mixture Model (GMM) applied to STRAIGHT, quality of converted speech is degraded because the converted spectrum is exceedingly smoothed. In this paper, we propose the GMM-based algorithm with dynamic frequency warping to avoid the over-smoothing. We also propose an addition of the weighted residual(More)
This paper describes a novel spectral conversion method for the voice transformation. We perform spectral conversion between speakers using a Gaussian Mixture Model (GMM) on joint probability density of source and target features. A smooth spectral sequence can be estimated by applying maximum likelihood (ML) estimation using dynamic features to the(More)