4 accepted papers
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly. Not only amplitude spectra but also phase spectra obtained from generated speech wav…
Voice conversion (VC) is a technique where only speaker-specific information in source speech is converted while preserving the associated phonological information. Nonnegative Matrix Factorization (NMF)-based VC has been researched because of the natural-sounding voice it produces compared with con…
This paper presents a novel probabilistic model that represents a joint probability of two visible variables with a deep architecture, called a deep relational model (DRM). The model stacks several layers from one visible layer on to another visible layer, sandwiching hidden layers between them. As…
In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. VC is a technique where only speaker specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on…