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.

本文介绍了 DailyTalk 这一高质量的会话 TTS 数据集，该数据集包含来自开放领域对话数据集 DailyDialog 的 2534 个对话，并扩展了 LST-CNN-TTS 模型，结果表明 DailyTalk 数据集可用作通用 TTS 数据集，并且基线模型可以表示 DailyTalk 中的上下文信息。