This article presents a method of sequence-to-sequence (seq2seq) voice conversion using
non-parallel training data. In this method, disentangled linguistic and speaker …

This article proposes a voice conversion (VC) method using sequence-to-sequence
(seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also …

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq),
non-parallel voice conversion approach, which utilizes text supervision during training. In …

Voice conversion (VC) refers to transforming the speaker characteristics of an utterance
without altering its linguistic contents. Many works on voice conversion require to have …

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-
sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those …

We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based
on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models …

This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice
conversion challenge (VCC) 2020. We consider a naive approach for voice conversion (VC) …

Any-to-any voice conversion problem aims to convert voices for source and target speakers,
which are out of the training data. Previous works wildly utilize the disentangle-based …

This paper proposes a voice conversion (VC) method based on a sequence-to-sequence
(S2S) learning framework, which enables simultaneous conversion of the voice …

In this paper, a neural network named sequence-to-sequence ConvErsion NeTwork
(SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT …