62 citations · 655 across the 97 of their papers we have counts for
4 papers · 2 filters
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
Yiming Wang, Tongfei Chen, Hainan Xu +7
We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neu…
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…
Building a mixed-lingual neural TTS system with only monolingual data
Liumeng Xue, Wei Song, Guanghui Xu +2
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. This paper…