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

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.

FPUAS : Fully Parallel UFANS-based End-to-End Acoustic System with 10x Speed Up

A lightweight end-to-end acoustic system is crucial in the deployment of text-to-speech tasks. Finding one that produces good audios with small time latency and fewer errors remains a problem. In this paper, we propose a new non-autoregressive, fully parallel acoustic system that utilizes a new attention structure and a recently proposed convolutional structure. Compared with the most popular end-to-end text-to-speech systems, our acoustic system can produce equal or better quality audios with fewer errors and reach at least 10 times speed up of inference.