论文标题
DailyTalk：用于对话文本到语音的对话数据集
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
论文作者
论文摘要
当前的大多数文本到语音（TTS）数据集是单个话语的集合，几乎没有对话方面。在本文中，我们介绍了DailyTalk，这是一个专为对话tts设计的高质量对话语音数据集。我们从开放域对话数据集Dabordialog中取样，修改并记录了2,541个对话，以继承其注释的属性。在我们的数据集之外，我们将先前的工作扩展为我们的基准，在该基线中，非自动回忆TTS的对话中的历史信息为条件。从一般和我们的新型指标的基线实验中，我们表明每日talk可以用作一般的TTS数据集，而且我们的基线还可以代表来自DailyTalk的上下文信息。 DailyTalk数据集和基线代码可自由使用CC-BY-SA 4.0许可证。
The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects. In this paper, we introduce DailyTalk, a high-quality conversational speech dataset designed for conversational TTS. We sampled, modified, and recorded 2,541 dialogues from the open-domain dialogue dataset DailyDialog inheriting its annotated attributes. On top of our dataset, we extend prior work as our baseline, where a non-autoregressive TTS is conditioned on historical information in a dialogue. From the baseline experiment with both general and our novel metrics, we show that DailyTalk can be used as a general TTS dataset, and more than that, our baseline can represent contextual information from DailyTalk. The DailyTalk dataset and baseline code are freely available for academic use with CC-BY-SA 4.0 license.
