The formal or “literary” type, still largely consistent with Tamil grammar Pavanandi’s standards set in the thirteenth century, and it is used in almost all written media, as well as some high-register functions. Tamil is a diglossia language which has two varieties that are used in different conditions. There are over 68 million native speakers of Tamil. There are also large Tamil-speaking populations elsewhere, including Sri Lanka, Malaysia, and Singapore, where Tamil has the status of a national and official language. Tamil is one of the Dravidian languages which is spoken mostly in the state of Tamil Nadu and Puducherry where it is the only official language. Section 5 concludes the paper and provides future research directions.Section 4 consists of the testing results for the proposed application.Sections 3 describes the proposed application.Section 2 includes the literature review including the introductions to Tamil and Vietnamese languages, fundamentals of speech recognition, and how the speech-to-text applications work as well as their benefits and challenges.The structure of the paper is as follows: This paper is an extended version of and aims to apply the Conceptual Framework to propose a web-based application that converts Tamil and Vietnamese speech to text without the effect of code-mixing and code switching. On the other hand, the human brain uses parallel array of simple processing elements (e.g., neurons) bound by weight (e.g., synapses which are changed with the individual experience to support the integration of multiple constraints). Tebelskis mentioned that computers use a complex processor with specific instructions and local memory. It is no doubt that the human brain and the conventional computer work differently using different paradigms. However, even after decades of work, computers performing speech recognition is still not compared to the level of human performance and it is still an area that needs further research and innovation. There have been substantial efforts since the 1950’s to lead the work on automatic speech recognition. There are many applications that can benefit from the speech interface such as dictation tools and any spoken database querying. It is widely noticed that people are comfortable with speech that many users prefer to communicate with computers using voice, rather than resorting to simplistic interfaces like keyboards and pointing tools. All the different individual effects can cause speech recognition to be more complex than speech generation. Also, electrical, and electronic features such as telephone and electronic devices may affect signal transmission from one node to another node. Furthermore, during transmission, background noise and echoes may affect and cause distortion in the speech pattern. These factors can affect human voice, accent, pronunciation, tone, and device’s volume. Some of the human organs such as vocal tracks and articulators have features that are nonlinear – and are being influenced by factors such as human gender, educational achievement, and human emotions. In fact, speech is regarded as one of the primary faculties of language, e.g., innate, and biologically determined, which means that people start to communicate without the help of anyone. In early childhood, without guidance, people normally learn all the relevant skills and continue to rely on speech communication throughout their lives. According to Tebelskis, speech is a natural means by which people use as a communication tool. However, there are different levels of ability and complexities among the speech to text applications which are based on the machine learning used by each application. These applications can be accessed on desktops, smart phones, or tablets to support the users and make their lives easier. There is a growing interest in speech to text applications as they can be used for a variety of functions in different areas such as education, business, machine translation, information retrieval, and document classification.
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