  Previous work in phonetically-grounded language generation has mainly focused
on domains such as lyrics and poetry. In this paper, we present work on the
generation of tongue twisters - a form of language that is required to be
phonetically conditioned to maximise sound overlap, whilst maintaining semantic
consistency with an input topic, and still being grammatically correct. We
present \textbf{TwistList}, a large annotated dataset of tongue twisters,
consisting of 2.1K+ human-authored examples. We additionally present several
benchmark systems (referred to as TwisterMisters) for the proposed task of
tongue twister generation, including models that both do and do not require
training on in-domain data. We present the results of automatic and human
evaluation to demonstrate the performance of existing mainstream pre-trained
models in this task with limited (or no) task specific training and data, and
no explicit phonetic knowledge. We find that the task of tongue twister
generation is challenging for models under these conditions, yet some models
are still capable of generating acceptable examples of this language type.
