# A Preliminary Study of Model-Generated Speech
## 2 Related Work
This model was initially developed to enhance machine translation technology, allowing machines to discover and learn how to map a sentence from one language to its corresponding sentence in another language. The Named Entity Workshop (NEWS) was established in Singapore in 2009 by the Agency for Science, Technology, and Research (A*STAR) to develop machine transliteration techniques. The NEWS compiles high-quality multilingual datasets for machine transliteration and defines metrics to evaluate performance. In this study, we have attempted to adopt statistical and deep learning machine transliteration methods to train models for sound changes in the Chaoshan dialect and utilize the metrics from the NEWS to evaluate their performance. Typologically, linguists collected data to extract shared innovative features, which were judged to have the same/different ancient languages. Take the implementation of Chinese languages as an example; [PERSON]. [31; 32; 33; 34] have carried out a series of studies on the relationships between Chinese dialects. In [32]'s work, [PERSON]'s correlation coefficients are employed within the statistical analysis to compute the presence of vocabulary and phonetic forms, categorized by the existence of sounds, rhymes, and tones. The significance of quantitative calculation methodologies in language classification resides in their establishment of a framework for gauging dialectical proximity, therefore offering a systematic approach to delineating dialect groupings. Additionally, phonological characteristics were scrutinized across 17 dialects, focusing on initial consonants [\(\mathrm{p}\), \(\mathrm{p}^{\mathrm{h}}\), and \(\mathrm{b}\)], tracing their descent from Middle Chinese. This examination encompassed considerations of nucleus (vowels), lips (rounded/unrounded), and mouths (open/closed), elucidating the classification of rimes. Tonal aspects were analyzed based on the four tones of flat, rising, falling, and entering, alongside three distinct categories of initial consonants: voiceless consonants, voiced stops and sibilants, and sonorants. It is acknowledged that the voiced or voiceless nature of the initial consonant influences the tonal pattern, a phenomenon rooted in ancient Chinese phonology. Most phonologists concur that the characteristics of the initial consonant interact with tonal variations. As a result, ref. [32] reported that Min languages exhibit greater mutual affinity compared to Hakka and Gan languages. Concerning the tones, ref. [31] observes a quantitative correlation between the pitch characteristics of yin and yang tones and the phonetic attributes of initial consonants. In an inventory encompassing 3433 dialects within 737 distinct linguistic variations, it was revealed that high tones predominate in most dialects. The falling tones are the most prevalent contour tone, while bi-directional tones are comparatively rare, which is categorized by [35] as a more marked tone. There is an observed trend wherein the dialects situated further south tend to exhibit a greater diversity of tones, whereas those located farther north tend to possess fewer tones [31]. Ref. [34] conducted a comparative analysis of vowel rhyme structures between calculated representations and the contemporary Beijing dialect. This analysis employed the notion of \"communication degree\" as a criterion for evaluating phonological historical constructs. For two dialects, A and B, the communication degree was determined by examining cognate words in A and B, with A serving as the source and B as the target, utilizing the \"one-way communication degree\" criterion. Each phonetic element, including initial consonants, glides, vowels, endings, and tones, is assigned a basic weight of one out of five, with positive (informational) and negative (noisy) values attributed accordingly. By multiplying these weights, the one-way communication degree is computed, enabling a comparison of the relative relationships between dialects.