The Duke Entry for 2020 Blizzard Challenge

Zexin Cai, Ming Li
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Abstract

This paper presents the speech synthesis system built for the 2020 Blizzard Challenge by team ‘H’. The goal of the challenge is to build a synthesizer that is able to generate high-fidelity speech with a voice that is similar to the one from the provided data. Our system mainly draws on end-to-end neural networks. Specifically, we have an encoder-decoder based prosody prediction network to insert prosodic annotations for a given sentence. We use the spectrogram predictor from Tacotron2 as the end-toend phoneme-to-spectrogram generator, followed by the neural vocoder WaveRNN to convert predicted spectrograms to audio signals. Additionally, we involve finetuning strategics to improve the TTS performance in our work. Subjective evaluation of the synthetic audios is taken regarding naturalness, similarity, and intelligibility. Samples are available online for listening. 1
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杜克大学参加2020暴雪挑战赛
本文介绍了由“H”团队为2020暴雪挑战赛构建的语音合成系统。挑战的目标是构建一个能够生成高保真语音的合成器,其声音与所提供数据中的声音相似。我们的系统主要利用端到端神经网络。具体来说,我们有一个基于编码器-解码器的韵律预测网络,为给定的句子插入韵律注释。我们使用Tacotron2的频谱图预测器作为端到端音素到频谱图的生成器,然后使用神经声码器WaveRNN将预测的频谱图转换为音频信号。此外,我们还涉及微调策略,以提高我们工作中的TTS性能。对合成音频的自然性、相似性和可理解性进行主观评价。样本可在线收听。1
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The Ajmide Text-To-Speech System for Blizzard Challenge 2020 The HITSZ TTS system for Blizzard challenge 2020 The SHNU System for Blizzard Challenge 2020 Submission from SRCB for Voice Conversion Challenge 2020 The UFRJ Entry for the Voice Conversion Challenge 2020
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