论文标题
MHTTS：与不完美转录的自发语音的快速多头文本到语音
MHTTS: Fast multi-head text-to-speech for spontaneous speech with imperfect transcription
论文作者
论文摘要
基于神经网络的端到端文本到语音（TTS）极大地提高了综合语音的质量。尽管如何有效地使用大量自发性语音仍然是一个空旷的问题。在本文中，我们提出了MHTTS，这是一种快速的多扬声器TTS系统，可对转录错误和说话风格的语音数据进行鲁棒性。具体而言，我们通过手动转录引入了一个多头模型和转移文本信息，并通过共同训练它们，并通过手动转录到自发的语音。 MHTTS具有三个优点：1）我们的系统以更快的推理速度合成了更好质量的多演讲声音。 2）我们的系统能够将正确的文本信息传输到使用不完美的转录，使用腐败模拟或由自动语音识别器（ASR）提供的数据。 3）我们的系统可以使用不完美的转录和综合表达语音利用大量的真实自发语音。
Neural network based end-to-end Text-to-Speech (TTS) has greatly improved the quality of synthesized speech. While how to use massive spontaneous speech without transcription efficiently still remains an open problem. In this paper, we propose MHTTS, a fast multi-speaker TTS system that is robust to transcription errors and speaking style speech data. Specifically, we introduce a multi-head model and transfer text information from high-quality corpus with manual transcription to spontaneous speech with imperfectly recognized transcription by jointly training them. MHTTS has three advantages: 1) Our system synthesizes better quality multi-speaker voice with faster inference speed. 2) Our system is capable of transferring correct text information to data with imperfect transcription, simulated using corruption, or provided by an Automatic Speech Recogniser (ASR). 3) Our system can utilize massive real spontaneous speech with imperfect transcription and synthesize expressive voice.
