Deep learning Text-to-Speech (TTS) systems have achieved impressive generated speech
quality, close to human parity. However, they suffer from training stability issues and in …

Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent
results, they typically require a sizable set of high-quality< text, audio> pairs for training …

Recently, end-to-end (E2E) neural text-to-speech systems, such as Tacotron2, have begun
to surpass the traditional multi-stage hand-engineered systems, with both simplified system …

End-to-end text-to-speech (TTS) synthesis is a method that directly converts input text to
output acoustic features using a single network. A recent advance of end-to-end TTS is due …

Tacotron-based text-to-speech (TTS) systems directly synthesize speech from text input.
Such frameworks typically consist of a feature prediction network that maps character …

In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models
have shown synthesis quality close to human speech. For example, FastSpeech2 transforms …

Although neural end-to-end text-to-speech models can synthesize highly natural speech,
there is still room for improvements to its efficiency and naturalness. This paper proposes a …

This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model
using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism …

With the number of smart devices increasing, the demand for on-device text-to-speech (TTS)
increases rapidly. In recent years, many prominent End-to-End TTS methods have been …

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded
in generating human-like speech, there is still room for improvements to its naturalness and …