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There are many languages (notably English) which do not have straightforward one-to-one rules between writing and pronunciation; therefore, the first step in text-to-speech generation has to be text-to-phoneme translation. input text is translated into pronunciation phonemes (e.g. input text xerox is translated into zi@r0ks for pronunciation).
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]
Google is showing off Translatotron, a first-of-its-kind translation model that can directly convert speech from one language into another while maintaining a speaker's voice and cadence.
Google Translate previously first translated the source language into English and then translated the English into the target language rather than translating directly from one language to another. [11] A July 2019 study in Annals of Internal Medicine found that "Google Translate is a viable, accurate tool for translating non–English-language ...
It is necessary to collect clean and well-structured raw audio with the transcripted text of the original speech audio sentence. Second, the text-to-speech model must be trained using these data to build a synthetic audio generation model. Specifically, the transcribed text with the target speaker's voice is the input of the generation model.
The generated translation utterance is sent to the speech synthesis module, which estimates the pronunciation and intonation matching the string of words based on a corpus of speech data in language B. Waveforms matching the text are selected from this database and the speech synthesis connects and outputs them.
The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.
MBROLA is speech synthesis software as a worldwide collaborative project. The MBROLA project web page provides diphone databases for many [1] spoken languages.. The MBROLA software is not a complete speech synthesis system for all those languages; the text must first be transformed into phoneme and prosodic information in MBROLA's format, and separate software (e.g. eSpeakNG) is necessary.