Laughing, sighing, stuttering, and other forms of paralanguage do not
contribute any direct lexical meaning to speech, but they provide crucial
propositional context that aids semantic and pragmatic processes such as irony.
It is thus important for artificial social agents to both understand and be
able to generate speech with semantically-important paralanguage. Most speech
datasets do not include transcribed non-lexical speech sounds and disfluencies,
while those that do are typically multi-speaker datasets where each speaker
provides relatively little audio. This makes it challenging to train
conversational Text-to-Speech (TTS) synthesis models that include such
paralinguistic components.
We thus present DisfluencySpeech, a studio-quality labeled English speech
dataset with paralanguage. A single speaker recreates nearly 10 hours of
expressive utterances from the Switchboard-1 Telephone Speech Corpus
(Switchboard), simulating realistic informal conversations. To aid the
development of a TTS model that is able to predictively synthesise paralanguage
from text without such components, we provide three different transcripts at
different levels of information removal (removal of non-speech events, removal
of non-sentence elements, and removal of false starts), as well as benchmark
TTS models trained on each of these levels.

研究表明，非语言语音如笑声、叹息、结巴等虽不直接提供词汇含义，但其提供的语义和语用上下文对理解其间的讽刺等有重要作用。本研究提出了 DisfluencySpeech，一个以真实对话模拟形式重现 Switchboard-1 电话语音语料库中出色演讲的英语语音数据集，为开发能够预测性地从文本中合成非语言语音的 TTS 模型提供了帮助。