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15.ai was a free non-commercial web application that used artificial intelligence to generate text-to-speech voices of fictional characters from popular media. [1] Created by an artificial intelligence researcher known as 15 during their time at the Massachusetts Institute of Technology, the application allowed users to make characters from video games, television shows, and movies speak ...
Apps such as textPlus and WhatsApp use Text-to-Speech to read notifications aloud and provide voice-reply functionality. Google Cloud Text-to-Speech is powered by WaveNet, [5] software created by Google's UK-based AI subsidiary DeepMind, which was bought by Google in 2014. [6] It tries to distinguish from its competitors, Amazon and Microsoft. [7]
The first version of the Microsoft Speech API was released for Windows NT 3.51 and Windows 95 in 1994, it was then part of Windows up to Windows Vista. This initial version already contained Direct Speech Recognition and Direct Text To Speech APIs which applications could use to directly control engines, as well as simplified 'higher-level ...
In 2009, film critic Roger Ebert employed CereProc to create a synthetic version of his voice. Ebert had lost the power of speech following surgery to treat thyroid cancer. CereProc mined tapes and DVD commentaries featuring Ebert's voice to create a text-to-speech voice that sounded more like his own. [4] Roger Ebert used the voice in his ...
This is an accepted version of this page This is the latest accepted revision, reviewed on 17 January 2025. Artificial production of human speech Automatic announcement A synthetic voice announcing an arriving train in Sweden. Problems playing this file? See media help. Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech ...
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eSpeak is a free and open-source, cross-platform, compact, software speech synthesizer.It uses a formant synthesis method, providing many languages in a relatively small file size. eSpeakNG (Next Generation) is a continuation of the original developer's project with more feedback from native speakers.
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.
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