Phonotactic Complexity and its Trade-offs
Table 5: Complexities for original and artificial languages when removing final-obstruent devoicing. † represents an statistically significant difference with p<0.05
['[BOLD] Model', '[BOLD] Complexity Orig', '[BOLD] Complexity Art', '[BOLD] Diff']
After deriving two artificial languages without obstruent devoicing from both German and Dutch, we used 10 fold cross validation to train models for each language. The statistical relevance of differences between normal and artificial languages was analyzed using paired permutation tests between the pairs. We see that the n-gram can capture this change in complexity for Dutch, but not for German. At the same time, the LSTM shows a statistically significant increase of ≈0.034 bits per phoneme when we remove obstruent devoicing from both languages. Fig. Average complexity increased by ≈0.62 bits per phoneme (an approximate 16% entropy increase), as measured by our LSTM models.