Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

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 …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

Despite the progress in voice conversion, many-to-many voice conversion trained on non-
parallel data, as well as zero-shot voice conversion, remains under-explored. Deep style …

Non-parallel voice conversion (VC) is a technique for learning the mapping from source to
target speech without relying on parallel data. This is an important task, but it has been …

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 …

Non-parallel many-to-many voice conversion remains an interesting but challenging speech
processing task. Many style-transfer-inspired methods such as generative adversarial …

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 review summarises the status of silent speech interface (SSI) research. SSIs rely on non-
acoustic biosignals generated by the human body during speech production to enable …

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 …