Neural network (NN) based voice conversion, which employs a nonlinear function to map the features from a source to a target speaker, has been shown to outperform GMM-based voice conversion approach [4-7]. However, there are still limitations to be overcome in NN-based voice conversion, e.g. NN is trained on a Frame Error (FE) minimization criterion and the corresponding weights are adjusted to...

New refinement schemes for voice conversion are proposed in this paper. We take mel-frequency cepstral coefficients (MFCC) as the basic feature and adopt cepstral mean subtraction to compensate the channel effects. We propose S/U/V (Silence/Unvoiced/Voiced) decision rule such that two sets of codebooks are used to capture the difference between unvoiced and voiced segments of the source speaker...

This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of speaker space. In voice conversion studies, realization of conversion from/to an arbitrary speaker’s voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC) based on an eigenvoice Gaussian mixture model (EV-GMM) was proposed. In the EVC, similarly t...

Voices can be deliberately disguised by means of human imitation or voice conversion. The question arises to what extent they can be modified by using either method. In the current paper, a set of speaker identification experiments are conducted; first, analysing some prosodic features extracted from voices of professional impersonators attempting to mimic a target voice and, second, using both...

In this paper, we describe an approach to voice characteristics conversion for an HMM-based text-to-speech synthesis system. Since this speech synthesis system uses phoneme HMMs as speech units, voice characteristics conversion is achieved by changing HMM parameters appropriately. To transform the voice characteristics of synthesized speech to the target speaker, we applied MAP/VFS algorithm to...

This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of speaker space. In voice conversion studies, realization of conversion from/to an arbitrary speaker’s voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC) based on an eigenvoice Gaussian mixture model (EV-GMM) was proposed. In the EVC, similarly t...

In this paper, we present linear transformation algorithms for many to one voice conversion (VC). Many to one VC is a tech nique for converting an arbitrary source speaker’s voice into the target speaker’s voice. A conversion model previously devel oped between many prestored source speakers and the target speaker is adapted into a new source speaker in an unsuper vised manner. In this study, w...

This paper presents techniques to improve the quality of voices generated through statistical singing voice conversion with direct waveform modification based on spectrum differential (DIFFSVC). The DIFFSVC method makes it possible to convert singing voice characteristics of a source singer into those of a target singer without using vocoder-based waveform generation. However, quality of the co...

Although all conventional voice conversion approaches require equivalent training utterances of source and target speaker, several recently proposed applications call for breaking this demand. In this paper, we present an algorithm which finds corresponding time frames within unaligned training data. The performance of this algorithm is tested by means of a voice conversion framework based on l...

In this paper, a voice conversion approach that combines two distinct ideas is proposed to improve the converted-voice quality. The first idea is to map spectral features, e.g. discrete cepstrum coefficients (DCC), with segmental Gaussian mixture models (GMMs). That is, a single GMM of a large number of mixture components is replaced here with several voice-content specific GMMs each consisting...