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Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
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.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 custom ...
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.
Dragon NaturallySpeaking uses a minimal user interface. As an example, dictated words appear in a floating tooltip as they are spoken (though there is an option to suppress this display to increase speed), and when the speaker pauses, the program transcribes the words into the active window at the location of the cursor.
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Speech synthesis includes text-to-speech, which aims to transform the text into acceptable and natural speech in real-time, [33] making the speech sound in line with the text input, using the rules of linguistic description of the text. A classical system of this type consists of three modules: a text analysis model, an acoustic model, and a ...
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DECtalk demo recording using the Perfect Paul and Uppity Ursula voices. DECtalk [4] was a speech synthesizer and text-to-speech technology developed by Digital Equipment Corporation in 1983, [1] based largely on the work of Dennis Klatt at MIT, whose source-filter algorithm was variously known as KlattTalk or MITalk.
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