  In Mandarin text-to-speech (TTS) system, the front-end text processing module
significantly influences the intelligibility and naturalness of synthesized
speech. Building a typical pipeline-based front-end which consists of multiple
individual components requires extensive efforts. In this paper, we proposed a
unified sequence-to-sequence front-end model for Mandarin TTS that converts raw
texts to linguistic features directly. Compared to the pipeline-based
front-end, our unified front-end can achieve comparable performance in
polyphone disambiguation and prosody word prediction, and improve intonation
phrase prediction by 0.0738 in F1 score. We also implemented the unified
front-end with Tacotron and WaveRNN to build a Mandarin TTS system. The
synthesized speech by that got a comparable MOS (4.38) with the pipeline-based
front-end (4.37) and close to human recordings (4.49).
