A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation
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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.
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