# A Preliminary Study of Model-Generated Speech
## 2 Related Work  
In computational linguistics, automated models focusing on language evolution primarily concentrate on detecting cognates among different related languages [19; 20; 21; 22; 23] and inferring phonological correspondences [24]. These efforts aim to construct phylogenetic trees representing the relationships between languages. However, the objective is to identify the rules of sound change rather than to actually build a model capable of predicting the historical evolution of language sounds. A similar task is found in machine transliteration, which phonetically converts the words of a source language into the words of a target language. Machine transliteration can be regarded as a subtask of machine translation. Many different approaches to machine translation have been adopted to solve the task of machine transliteration. Early approaches were based on dictionaries or lexicons to map the phonemes between the source and target languages [25; 26]. Later, statistical machine learning methods were adopted to learn the mapping from the source to the target languages [27; 28]. Statistical machine learning constitutes a foundational framework for the domain of machine learning, deriving its principles from the disciplines of statistics and functional analysis. This theoretical construct is primarily concerned with the challenge of statistical inference, specifically the derivation of a predictive function from a given dataset. The implementation of statistical learning theory has precipitated significant advancements across a variety of domains, including but not limited to computer vision, speech recognition, and bioinformatics. Through its rigorous approach to understanding and modeling the underlying patterns within data, statistical learning theory has played a pivotal role in the development and enhancement of algorithms that facilitate complex decision-making and predictive analyses in these fields. Ref. [27] proposed a statistical machine learning framework that facilitates direct orthographical mapping (DOM) between two distinct languages via a joint source-channel model, herein referred to as the n-gram Transliteration Model (TM). The n-gram TM model streamlines the orthographic alignment process by automatically generating aligned transliteration units from a bilingual dictionary. Employing the n-gram TM within the DOM framework significantly diminishes the effort required for system development and achieves a substantial enhancement in transliteration accuracy, surpassing the performance of contemporary state-of-the-art machine learning algorithms. The efficacy of this modeling framework is corroborated through a series of experimental validations focusing on the transliteration between the English and Chinese language pair. Ref. [28] utilized conditional random field (CRF) models to formulate transliteration as a sequence-labeling problem. The many-to-many (m2m) aligner was used to generate character mappings between English and Arabic, and then a CRF model was trained based on the alignment results to label each English input character with a sequence of Arabic characters. CRFs are a class of statistical modeling methods often applied in pattern recognition and machine learning and are used for structured prediction. Diverging from traditional classifiers, which determine labels for individual samples in isolation, CRFs are designed to incorporate contextual information, considering the interdependencies among adjacent samples. This contextual consideration is operationalized through the formulation of a graphical model, encapsulating the dependencies among predictions. The architecture of the graph utilized is contingent upon the specific requirements of the application at hand. In recent decades, deep learning-based models have been widely adopted for machine transliteration. Deep learning is the subset of machine learning methods based on artificial neural networks (ANNs) with representation learning. The adjective \"deep\" refers to the use of multiple layers in the network.