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 …

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

Despite recent progress in generative adversarial network (GAN)-based vocoders, where
the model generates raw waveform conditioned on acoustic features, it is challenging to …

The research community has long studied computer-assisted pronunciation training (CAPT)
methods in non-native speech. Researchers focused on studying various model …

This article aims to improve neural TTS with vector-quantized, compact speech
representations. We propose a Vector-Quantized Variational AutoEncoder (VQ-VAE) based …

We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data
augmentation via voice conversion. We assume to have a corpus of neutral non-expressive …

Whilst recent neural text-to-speech (TTS) approaches produce high-quality speech, they
typically require a large amount of recordings from the target speaker. In previous work, a 3 …

Creating realistic and natural-sounding synthetic speech remains a big challenge for voice
identities unseen during training. As there is growing interest in synthesizing voices of new …

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a
variant of the standard version (L1), which is challenging as L2 is different from L1 in terms …

Non-parallel voice conversion (VC) is typically achieved using lossy representations of the
source speech. However, ensuring only speaker identity information is dropped whilst all …