185 citations · 262 across the 19 of their papers we have counts for
4 papers · 1 filter
Forward-Backward Decoding for Regularizing End-to-End TTS
Yibin Zheng, Xi Wang, Lei He +4
Neural end-to-end TTS can generate very high-quality synthesized speech, and even close to human recording within similar domain text. However, it performs unsatisfactory when scal…
A New GAN-based End-to-End TTS Training Algorithm
Haohan Guo, Frank K. Soong, Lei He +1
End-to-end, autoregressive model-based TTS has shown significant performance improvements over the conventional one. However, the autoregressive module training is affected by the…
Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
Haohan Guo, Frank K. Soong, Lei He +1
The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS. However, its predict…
Feature reinforcement with word embedding and parsing information in neural TTS
Huaiping Ming, Lei He, Haohan Guo +1
In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework. The proposed method utilizes the multiple i…