Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization). The adoption of these methods is still limited by the fact that most speech synthesis datasets are read, severely limiting spontaneity and expressivity. Here, we introduce Expresso, a high-quality expressive speech dataset for textless speech synthesis that includes both read speech and improvised dialogues rendered in 26 spontaneous expressive styles. We illustrate the challenges and potentials of this dataset with an expressive resynthesis benchmark where the task is to encode the input in low-bitrate units and resynthesize it in a target voice while preserving content and style. We evaluate resynthesis quality with automatic metrics for different self-supervised discrete encoders, and explore tradeoffs between quality, bitrate and invariance to speaker and style. All the dataset, evaluation metrics and baseline models are open source

我们介绍了Expresso，这是一个高质量的表达性语音数据集，用于无文字语音合成，其中包括使用26种自发表达的风格渲染的朗读语音和即兴对话。我们通过一个表达性再合成基准来说明该数据集的挑战和潜力，其中的任务是在保持内容和风格的同时，使用低比特率单位对输入进行编码并在目标语音中重新合成。我们使用自动度量标准评估了不同自我监督离散编码器的合成质量，并探讨了质量、比特率和对说话人和风格的不变性之间的权衡。所有数据集、评估指标和基线模型均为开源。

EXPRESSO：离散表达性语音重新合成的基准和分析