6 accepted papers
In this paper, we propose a cross-lingual voice conversion (VC) scheme leveraging non-parallel and parallel methodologies. The goal of cross-lingual VC is to transform the voice of one speaker from a language dataset into the voice of another speaker from a different language dataset. First, two non…
Text-to-Speech (TTS) systems have been evolving rapidly in recent years. With the great modelling power of deep neural networks, researchers have achieved end-to-end conversion from raw text to speech. It has been shown by various research projects that end-to-end TTS systems are able to generate sp…
A cross-stream dependence modelling (CSDM) method has been proposed to model the dependence of spectral distributions on F0 observations for hidden Markov model (HMM) based speech synthesis. However, this method incorporates CSDM only for the embedded training of HMM estimation while ignoring CSDM i…
Automatic detection of pathological voice is a challenging task in speech processing. Appropriate acoustic cues of voice can be used to differentiate between normal voices and pathological voices. We propose a method to represent each speech utterance using three types of speech signal representatio…
Voice conversion (VC) aims to make one speaker (source) to sound like spoken by another speaker (target) without changing the language content. Most of the state-of-the-art voice conversion systems focus only on timbre conversion. However, the speaker identity is characterized by the source-related…
This paper presents a sparse representation framework for weighted frequency warping based voice conversion. In this method, a frame-dependent warping function and the corresponding spectral residual vector are first calculated for each source-target spectrum pair. At runtime conversion, a source sp…