Building a voice conversion (VC) system from non-parallel speech corpora is challenging
but highly valuable in real application scenarios. In most situations, the source and the target …

We propose a flexible framework for spectral conversion (SC) that facilitates training with
unaligned corpora. Many SC frameworks require parallel corpora, phonetic alignments, or …

Although voice conversion (VC) algorithms have achieved remarkable success along with
the development of machine learning, superior performance is still difficult to achieve when …

In this paper, we present a novel technique for a non-parallel voice conversion (VC) with the
use of cyclic variational autoencoder (CycleVAE)-based spectral modeling. In a variational …

Voice conversion (VC) techniques can be abused by malicious parties to transform their
audios to sound like a target speaker, making it hard for a human being or a speaker …

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 …

We propose voice conversion model from arbitrary source speaker to arbitrary target
speaker with disentangled representations. Voice conversion is a task to convert the voice of …

We study the problem of cross-lingual voice conversion in non-parallel speech corpora and
one-shot learning setting. Most prior work require either parallel speech corpora or enough …

Text-to-speech synthesis (TTS) has progressed to such a stage that given a large, clean,
phonetically balanced dataset from a single speaker, it can produce intelligible, almost …

We address a nonparallel data-driven many-to-many speech modeling and multimodal style
conversion method. In this work, we train a speech conversion model for multiple domains …