# A Preliminary Study of Model-Generated Speech
## 2 Related Work
The ultimate finding indicates that in terms of communicative proficiency, the Beijing dialect holds the third position, trailing behind the Chengdu and Hankou dialects. Furthermore, ref. [34] inferred that the maximum mutual intelligibility between old and young generations, based on the index derived by [33], was typically 0.92, suggesting a generational discrepancy of up to 0.08. This discrepancy serves as a benchmark for mutual intelligibility. Moreover, the formula is extended to assess intelligibility among different dialects, where a high index indicates seamless communication and a lower index signifies potential communication barriers. For example, when the mutual intelligibility between Chengdu and Hankou reaches the highest level at 0.795, communication between speakers of these dialects poses no significant challenges. However, a decrease in mutual intelligibility to 0.475 between Beijing and Guangzhou dialects indicates potential communication difficulties [34]. By combining the findings of [33; 34], and if the life expectancy difference between two generations is about 100 years, the study extrapolates a mutual intelligibility loss of 0.08 per century, offering insights into historical language dynamics. Notably, this research contributes to our understanding of mid-phonological changes and dialectical relationships. Ref. [36] proposed and implemented a method for the study of historical phonetics. The main idea is that the speech database requires native speakers to provide a corpus,especially in cases where there has been a gradual decline in native speakers. We are left with a question: Without fieldwork, can a prediction by a model be used to fill in unknown pronunciations? Furthermore, can this help preserve cultural heritage in the future? They applied the method to the seven major Chinese languages of the Sino-Tibetan language family: Mandarin, Wu, Cantonese, Xiang, Hakka, Gan, and Fujian. Ref. [36] translated the pronunciation of each Chinese character into IPA and marked eight phonological characteristics: tonal category, tonal value, initials, mediations, vowels, diphthongs, nasalization, and final rhymes. They proposed a new generative model, which imported hidden random variables and mapped each phonological feature to the hidden random variables. The probability distribution of the random variables produced the various possible characters for each Chinese character using a Markov chain Monte Carlo method. Finally, they used the voice data as reference data to solve the actual value of the random variable in the generative model. This method can simultaneously use material from the middle-ancient rhyme book and the possibly incomplete dialect phonetic data to explore the superlingual rhymes. This research made three contributions: (1) in addition to the rhyme data, the dialect data are very helpful for predicting the phonetics of another dialect; (2) this generative model is more effective at predicting closely related dialect data; and (3) filling out the voice data through the proposed model can improve accuracy in predicting new dialect voices. Many previous studies have proposed adding missing speech [36] or constructing the phonetic forms of ancestral language [37]. However, while the models constructed by these methods can predict possible voice forms, they find it difficult to interpret and explore phonetic evolution. The objective of this investigation is to simulate the linguistic transformations within the Chaoshan region over the preceding century. This endeavor utilizes two dictionaries, one published in 1883 and the other in 2015, to elucidate the contemporary pronunciation dynamics during these periods. Employing the pronunciation data from 1883 as the source language and those from 2015 as the target language, the model extrapolates predicted pronunciations based on the 1883 dataset.