PANCETTA : Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically (2023)

Abstract: Tongue twisters are meaningful sentences that are difficult to pronounce. The process of automatically generating tongue twisters is challenging since the generated utterance must satisfy two conditions at once: phonetic difficulty and semantic meaning. Furthermore, phonetic difficulty is itself hard to characterize and is expressed in natural tongue twisters through a heterogeneous mix of phenomena such as alliteration and homophony. In this paper, we propose PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically. We leverage phoneme representations to capture the notion of phonetic difficulty, and we train language models to generate original tongue twisters on two proposed task settings. To do this, we curate a dataset called TT-Corp, consisting of existing English tongue twisters. Through automatic and human evaluation, as well as qualitative analysis, we show that PANCETTA generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters.

## introduction a tongue twister is a sentence which is both articulatorily difficult (i.e. colloquially speaking, hard to say or "twisting") while at the same time being meaningful and fluent. some examples of tongue twisters are shown in together with riddles, rhymes, fables, and other such creative artifacts, tongue twisters were historically often employed as a vehicle for early transmission of native language diction, grammar, and vocabulary to children, through parent-child interaction, playtime activity, and kindergarten instruction @xcite . tongue twisters have also been used as experimental aids for research studies of speech production in cognitive science and related disciplines, both among healthy speakers and those with speech and auditory disorders such as dysarthria @xcite . they are also used as pedagogic aids in speech therapy, as well as for treatment of speech disorders and psychological disorders relating to public speaking and elocution @xcite . an example of this was in a scene foot_0 from the king's @xcite , where george vi repeats a tongue twister during therapy to reduce his stutter. lastly, tongue twisters find use as teaching aids for english diction in efl (english as a foreign language) instructional settings @xcite . the coining of a novel, unique tongue twister which spreads sufficiently to become normative and well-recognized is rare, hence characterizing them as long-tailed linguistic phenomena @xcite . however, they are not limited to english, and are found across the world's languages, e.g., persian ("shish sikh jigar sikhi shi shezar.") @xcite and french ("cinq chiens chassent six chats.") consider the idealized scenario where we have a "mouth model" which a) maps different regions of the mouth, palate, and the larynx to the dictio- ## methodology as this is a new task, there are no existing methods that can easily generate novel tongue twisters. the main challenge is how to incorporate phonetic difficulty into our generations. to do so, we propose two baseline and two phoneme-aware models, which are applicable to both the tt-prompt and tt-keyword task settings (see ## related work automatic tongue twister generation is a largely unexplored task. existing systems mostly use synonym replacements @xcite on existing tongue twisters, which requires a large list of tongue twisters to begin with and cannot generate novel ones from scratch. @xcite generates tongue twisters using sound vectors, and joshipura (2020) trains an lstm on a small tongue twister dataset, but neither are able to produce novel and semantically coherent examples. furthermore, no methods currently exist for the tt-keyword task. there have been multiple studies on creative generation of various figures of speech such as similes @xcite , metaphors @xcite , and personifications @xcite . however, these other creative linguistic constructs don't require working with another modality in the same way that tongue twister generation relies on phonemes. among these creative linguistic constructs, the closest ones to tongue twisters would likely be alliterations @xcite , rhymes @xcite , and poetry @xcite . notably, deephaiku @xcite also explores using phonemes and a multitask objective in order to generate haikus. however, tongue twister generation goes beyond alliterations and rhymes; rather it is a mix of all these various techniques. in addition, it differs from poetry generation because poetry generation focuses on generating rhythmic verses and syllables, whereas the main focus of tongue twister generation is on phonetic difficulty. using phonemes in language modeling has been previously explored in the speech domain for automatic speech recognition @xcite @xcite . in this paper, we trained a bart model to do p2g translation. other existing methods include expectation maximization @xcite , a* search @xcite , and hidden markov models @xcite . there is also work on more general constrained text generation tasks. an example is @xcite , who propose semantic text exchange to adjust a text's topic-level semantics. lin et al. ## conclusion and future work in this paper, we proposed the task of automatic tongue twister generation, and explored it under two settings: tt-prompt and tt-keyword. we curated a dataset called tt-corp of 600+ english tongue twisters from various sources and proposed pancetta, a training methodology which incorporates phoneme representations. we implemented two variants: pancetta-p (phoneme), which trains a phoneme-based language model, and pancetta-j (joint), which jointly incorporates both phoneme-level information and graphemelevel information during training time. through empirical results and qualitative evaluations, we showed that incorporating phonemes is indeed helpful in producing effective tongue twisters which are harder to pronounce while staying fluent. while pancetta works well at generation, the generation process lacks interpretability. this is most notable when looking at the phonetic difficulty classifier. currently, the classifier does not identify and separately score elements of phonetic difficulty or come up with an explicit decomposition. we believe that such explicit decomposition can be very useful in the future for understanding more about tongue twisters and the "mouth model" discussed in §1. in addition, the procedure of fine-tuning a pretrained model in 4 different modes involving both graphemes and phonemes, devised here to fine-tune pancetta-j, can also be adopted more generally as a data augmentation @xcite ) method for lm finetuning, creating a (pseudo) count of 4n training examples given n initial ones. lastly, tongue twisters can potentially be incorporated in dialogue agents, adding creativity and personality @xcite .