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A speech translation system would typically integrate the following three software technologies: automatic speech recognition (ASR), machine translation (MT) and voice synthesis (TTS). The speaker of language A speaks into a microphone and the speech recognition module recognizes the utterance.
Voice-over translation is an audiovisual translation [1] technique in which, unlike in dubbing, actor voices are recorded over the original audio track which can be heard in the background. This method of translation is most often used in documentaries and news reports to translate words of foreign-language interviewees in countries where ...
Another recent challenge is the ADD [77] —Audio Deepfake Detection—which considers fake situations in a more real-life scenario. [78] Also the Voice Conversion Challenge [79] is a bi-annual challenge, created with the need to compare different voice conversion systems and approaches using the same voice data.
Also, voice cloning was used to translate podcast content into different languages using the podcaster's voice. [32] Another includes those who may have lost their voice can gain back a sense of individuality by creating their voice clone by inputting recordings of them speaking before they lost their voices. [33]
Use of a "do-not-translate" list, which has the same end goal – transliteration as opposed to translation. [39] still relies on correct identification of named entities. A third approach is a class-based model. Named entities are replaced with a token to represent their "class"; "Ted" and "Erica" would both be replaced with "person" class token.
A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [10] GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2]
Using a voice recognition system and a database, a robotic voice will recite the translation in the desired language. [4] Google's stated aim is to translate the entire world's information. Roya Soleimani, a spokesperson for Google, said during a 2013 interview demonstrating the translation app on a smartphone, "You can have access to the world ...