1 research outputs found
A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation
Chinese dialect text-to-speech(TTS) system usually can only be utilized by
native linguists, because the written form of Chinese dialects has different
characters, idioms, grammar and usage from Mandarin, and even the local speaker
cannot input a correct sentence. For Mandarin text inputs, Chinese dialect TTS
can only generate partly-meaningful speech with relatively poor prosody and
naturalness. To lower the bar of use and make it more practical in commercial,
we propose a novel Chinese dialect TTS frontend with a translation module. It
helps to convert Mandarin text into idiomatic expressions with correct
orthography and grammar, so that the intelligibility and naturalness of the
synthesized speech can be improved. A non-autoregressive neural machine
translation model with a glancing sampling strategy is proposed for the
translation task. It is the first known work to incorporate translation with
TTS frontend. Our experiments on Cantonese approve that the proposed frontend
can help Cantonese TTS system achieve a 0.27 improvement in MOS with Mandarin
inputs.Comment: 5 pages,5 figure