The majority of current TTS datasets, which are collections of individual utterances, contain few conversational aspects in terms of both style and metadata. In this paper, we introduce DailyTalk, a high-quality conversational speech dataset designed for Text-to-Speech. We sampled, modified, and recorded 2,541 dialogues from the open-domain dialogue dataset DailyDialog which are adequately long to represent context of each dialogue. During the data construction step, we maintained attributes distribution originally annotated in DailyDialog to support diverse dialogue in DailyTalk. On top of our dataset, we extend prior work as our baseline, where a non-autoregressive TTS is conditioned on historical information in a dialog. We gather metadata so that a TTS model can learn historical dialog information, the key to generating context-aware speech. From the baseline experiment results, we show that DailyTalk can be used to train neural text-to-speech models, and our baseline can represent contextual information. 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。

DailyTalk: 用于会话文本转语音的口语对话数据集