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 …

This paper introduces a new speech corpus called" LibriTTS" designed for text-to-speech
use. It is derived from the original audio and text materials of the LibriSpeech corpus, which …

We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech,
conditioned on fundamental frequency contours. The model predicts pitch contours during …

We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on
Tacotron that is able to produce high quality speech in multiple languages. Moreover, the …

Despite the ability to produce human-level speech for in-domain text, attention-based end-to-
end text-to-speech (TTS) systems suffer from text alignment failures that increase in …

This paper introduces PnG BERT, a new encoder model for neural TTS. This model is
augmented from the original BERT model, by taking both phoneme and grapheme …

Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech
(TTS) has drawn increasing attention. However, the works apply pre-training with character …

We propose Cotatron, a transcription-guided speech encoder for speaker-independent
linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be …

In this paper, we propose a phase reconstruction framework, named Deep Griffin-Lim
Iteration (DeGLI). Phase reconstruction is a fundamental technique for improving the quality …

End-to-end speech synthesis models directly convert the input characters into an audio
representation (eg, spectrograms). Despite their impressive performance, such models have …