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Neural network based end-to-end Text-to-Speech (TTS) has greatly improved thequality of synthesized speech. While how to use massive spontaneous speechwithout transcription efficiently still remains an open problem. In this paper,we propose MHTTS, a fast multi-speaker TTS system that is robust totranscription errors and speaking style speech data. Specifically, we introducea multi-head model and transfer text information from high-quality corpus withmanual transcription to spontaneous speech with imperfectly recognizedtranscription by jointly training them. MHTTS has three advantages: 1) Oursystem synthesizes better quality multi-speaker voice with faster inferencespeed. 2) Our system is capable of transferring correct text information todata with imperfect transcription, simulated using corruption, or provided byan Automatic Speech Recogniser (ASR). 3) Our system can utilize massive realspontaneous speech with imperfect transcription and synthesize expressivevoice.

MHTTS: Fast multi-head text-to-speech for spontaneous speech with imperfect transcription