DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
Keon Lee, Kyumin Park, Daeyoung Kim
Abstract
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
BibTeX
@inproceedings{icassp2023_dailytalkspokend,
title = {DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech},
author = {Keon Lee and Kyumin Park and Daeyoung Kim},
booktitle = {ICASSP 2023},
year = {2023}
}