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 …

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

Approaches based on deep neural networks have achieved striking performance when
testing data and training data share similar distribution, but can significantly fail otherwise …

Speech information can be roughly decomposed into four components: language content,
timbre, pitch, and rhythm. Obtaining disentangled representations of these components is …

We present an unsupervised non-parallel many-to-many voice conversion (VC) method
using a generative adversarial network (GAN) called StarGAN v2. Using a combination of …

This paper presents a review of techniques involved in the creation and detection of audio
deepfakes, the first section provides information about general deep fakes. In the second …

Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home
assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient …

The growing use of voice user interfaces has led to a surge in the collection and storage of
speech data. While data collection allows for the development of efficient tools powering …

Prosody plays an important role in characterizing the style of a speaker or an emotion, but
most non-parallel voice or emotion style transfer algorithms do not convert any prosody …

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) …