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Text normalization is frequently used when converting text to speech. Numbers, dates, acronyms, and abbreviations are non-standard "words" that need to be pronounced differently depending on context. [2] For example: "$200" would be pronounced as "two hundred dollars" in English, but as "lua selau tālā" in Samoan. [3]
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.
Transliteration is the process of representing or intending to represent a word, phrase, or text in a different script or writing system. Transliterations are designed to convey the pronunciation of the original word in a different script, allowing readers or speakers of that script to approximate the sounds and pronunciation of the original word.
A text-to-speech system (or "engine") is composed of two parts: [3] a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization.
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]
Open the HTML file in a text editor and copy the HTML source code to the clipboard. Paste the HTML source into the large text box labeled "HTML markup:" on the html to wiki page. Click the blue Convert button at the bottom of the page. Select the text in the "Wiki markup:" text box and copy it to the clipboard. Paste the text to a Wikipedia ...