  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.
