论文标题
pancetta：音素意识到神经完成，以自动引起舌头扭曲
PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
论文作者
论文摘要
舌头扭曲是很难发音的有意义的句子。自动产生舌头扭曲的过程具有挑战性，因为产生的话语必须立即满足两个条件：语音难度和语义含义。此外，语音困难本身很难表征，并且通过异质的现象（例如垂涎和谐音）的异质组合以自然的扭曲者表达。在本文中，我们提出了Pancetta：音素意识到的神经完成，以自动引起舌头扭曲。我们利用音素表示来捕获语音难度的概念，并训练语言模型以在两个建议的任务设置上生成原始的舌头扭曲。为此，我们策划了一个名为Pancetta的数据集，该数据集由现有的英语舌头旋转器组成。通过自动和人类评估以及定性分析，我们表明pancetta产生了新颖，语音上的困难，流利和语义上有意义的舌头扭曲。
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 PANCETTA, 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.
