In this paper we present a flexible deterministic plus stochastic model (DSM) approach for parametric speech analysis and synthesis with high quality. The novelty of the proposed speech processing system lies in its extended means to estimate the unvoiced stochastic component and to robustly handle the transformation of the glottal excitation source. It is therefore well suited as speech system...

This paper presents the NU-NAIST voice conversion (VC) system for the Voice Conversion Challenge 2016 (VCC 2016) developed by a joint team of Nagoya University and Nara Institute of Science and Technology. Statistical VC based on a Gaussian mixture model makes it possible to convert speaker identity of a source speaker’ voice into that of a target speaker by converting several speech parameters...

This paper presents a novel statistical sample-based approach for Gaussian Mixture Model (GMM)-based Voice Conversion (VC). Although GMM-based VC has the promising flexibility of model adaptation, quality in converted speech is significantly worse than that of natural speech. This paper addresses the problem of inaccurate modeling, which is one of the main reasons causing the quality degradatio...

Voice conversion systems aim to process speech from a source speaker so it would be perceived as spoken by a target speaker. This paper presents a procedure to improve high resolution voice conversion by modifying the algorithm used for residual estimation. The proposed residual estimation algorithm exploits the temporal dependencies between residuals in consecutive speech frames using a hidden...

This paper describes a novel approach based on voice conversion (VC) to speaker-adaptive speech synthesis for speech-tospeech translation. Voice quality of translated speech in an output language is usually different from that of an input speaker of the translation system since a text-to-speech system is developed with another speaker’s voices in the output language. To render the input speaker...

This paper reviews our proposed approach to voice conversion (VC) and voice quality control based on an eigenvoice technique. VC is a technique to modify nonlinguistic information such as speaker individuality while keeping linguistic information unchanged. In the traditional VC framework, a conversion model for a source and target speaker-pair needs to be trained in advance using a parallel da...

This work address the application of Voice Conversion to singing-voice. The GMM-based approach was applied to VOCALOID, a concatenative singing synthesizer, to perform singer timbre conversion. The conversion framework was applied to full-quality singing databases, achieving a satisfactory conversion effect on the synthesized utterances. We report in this paper the results of our experimentatio...

Voice conversion method is applied to synthesizing emotional speech from standard reading (neutral) speech. Pairs of neutral speech and emotional speech are used for conversion rule training. The conversion adopts GMM (Gaussian Mixture Model) with DFW (Dynamic Frequency Warping). We also adopt STRAIGHT, the high-quality speech analysis-synthesis algorithm. As conversion target emotions, (Hot) a...

This paper studies the inclusion of glottal source characteristics in voice conversion (VC) systems. We use source/filter decomposition to parametrize the vocal tract using LSF, the glottal source using the LF model, and the aspiration noise using amplitude-modulated high-pass filtered AWGN noise. To evaluate the impact of this new parametrization in VC, we use a reference conversion system tha...