The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Speaker identity is one of the important characteristics of human speech. In voice
conversion, we change the speaker identity from one to another, while keeping the linguistic …

In this paper, we first provide a review of the state-of-the-art emotional voice conversion
research, and the existing emotional speech databases. We then motivate the development …

For speaker recognition, it is difficult to extract an accurate speaker representation from
speech because of its mixture of speaker traits and content. This paper proposes a …

Non-parallel voice conversion (VC) is a technique for learning mappings between source
and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial …

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 …

Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while
preserving the linguistic content and speaker identity. In EVC, emotions are usually treated …

Emotional speech synthesis aims to synthesize human voices with various emotional effects.
The current studies are mostly focused on imitating an averaged style belonging to a specific …

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 …

Non-parallel voice conversion (VC) is a technique for training voice converters without a
parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and …