End-to-end text-to-speech (TTS) has shown great success on large quantities of paired text
plus speech data. However, laborious data collection remains difficult for at least 95% of the …

Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus,
which is troublesome to collect. In this paper, we propose a transfer learning framework for …

This work applies a hierarchical transfer learning to implement deep neural network (DNN)-
based multilingual text-to-speech (TTS) for low-resource languages. DNN-based system …

Recently, sequence-to-sequence models with attention have been successfully applied in
Text-to-speech (TTS). These models can generate near-human speech with a large …

In this paper, we present SANE-TTS, a stable and natural end-to-end multilingual TTS
model. By the difficulty of obtaining multilingual corpus for given speaker, training …

Text-to-Speech (TTS) aims to convert the input text to a human-like voice. With the
development of deep learning, encoder-decoder based TTS models perform superior …

We present a TTS neural network that is able to produce speech in multiple languages. The
proposed network is able to transfer a voice, which was presented as a sample in a source …

This paper studies a multilingual sequence-to-sequence textto-speech framework towards
universal modeling, that is able to synthesize speech for any speaker in any language using …

When deploying a Chinese neural text-to-speech (TTS) synthesis system, one of the
challenges is to synthesize Chinese utterances with English phrases or words embedded …

State-of-the-art text-to-speech (TTS) synthesis models can produce monolingual speech
with high intelligibility and naturalness. However, when the models are applied to synthesize …