# A Preliminary Study of Model-Generated Speech
## 1 Introduction  
With the rapid development of science and technology, data exploration techniques can now be applied to different levels of knowledge. The increasing sophistication of machine learning models enables the simulation of a growing array of phonetic changes, therefore mirroring real-world linguistic shifts. For example, the study conducted by [1] employs an agent-based model (ABM) methodology to investigate speech stability and change by utilizing authentic speech data from a cohort of speakers. The analysis encompasses two distinct speech databases, with the initial dataset comprising recordings of individuals across different age groups speaking Standard Southern British English (SSBE). By employing an ABM facilitated by unsupervised machine learning algorithms, the research identifies a progressive phonetic shift concerning the \(\mathrm{/u/}\) vowel sound. However, no significant phonological reclassification of any vowels was observed. These findings suggest a tendency towards phonetic alteration while maintaining phonological stability. Furthermore, the ABM approach was extended to examine diphthongs in New Zealand English over the preceding five decades, particularly focusing on the merger of \(\mathrm{/_{\mathrm{e0}}}\)/ into \(\mathrm{/_{\mathrm{10}}}\)/. This exploration delves into the principles of exemplar theory to elucidate the dynamics of phonetic change within the context of language evolution. As machine learning methodologies effectively capture these transformations, researchers in phonetics and phonology can leverage such simulations to scrutinize and refine existing theoretical frameworks of phonetic change. This comparative analysis serves to elucidate specific theories pertaining to the mechanisms underlying phonetic evolution. In complement to micro-level analyses of speech evolution, the macroscopic observation of historical linguistic changes represents another realm where machine learning methodologies can offer substantial contributions. In parallel, the history of sound changes in a language seems to be being investigated, but this has rarely been found in previous studies. One of the related fields is machine transliteration, which involves the phonetic conversion of words from a sourcelanguage into the words of a target language. Positioned as a subset of machine translation, it encompasses diverse methodological approaches aimed at addressing the intricacies of transliteration tasks. In that sense, utilizing a novel corpus automatic detection method could potentially bridge existing gaps by facilitating a comparative analysis between model-generated predictions and the derivational sequence of sound development. This parallel examination holds promise for enhancing our understanding of phonetic evolution and the predictive capabilities of computational models in phonological research. Indeed, a targeted approach involves leveraging established models to translate a given word from one contemporary pronunciation to another. By systematically analyzing the accurately generated words from such models in juxtaposition with the sound acquisition sequence observed in human beings, which typically progresses from unmarked features to marked ones, valuable insights into phonetic evolution and model performance can be gleaned. On the other hand, the evolution of the Chaoshan dialect within historical linguistic contexts underscores its origins and development into the contemporary dialect spoken today. Scholars such as [2; 3] assert that the Chaoshan dialect is part of the Southern Min dialect, which itself belongs to the broader Min dialect family. Historically, Min dialects have exhibited close linguistic ties with other Chinese dialects, yet they have also maintained a distinctiveness, rendering Min among the most conservative of all Chinese dialect groups [4]: p. 216). The Chaoshan dialect's formation can be traced back to around the Song Dynasty.