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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.
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 ...
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.
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 major steps in producing speech from text are as follows: Structure analysis: Processes the input text to determine where paragraphs, sentences, and other structures start and end. For most languages, punctuation and formatting data are used in this stage. Text pre-processing: Analyzes the input text for special constructs of the language.
The Festival Speech Synthesis System is a general multi-lingual speech synthesis system originally developed by Alan W. Black, Paul Taylor and Richard Caley [1] at the Centre for Speech Technology Research (CSTR) at the University of Edinburgh. Substantial contributions have also been provided by Carnegie Mellon University and other sites.
The second is a link to the article that details that symbol, using its Unicode standard name or common alias. (Holding the mouse pointer on the hyperlink will pop up a summary of the symbol's function.); The third gives symbols listed elsewhere in the table that are similar to it in meaning or appearance, or that may be confused with it;
Put text in small caps: set: Insert question mark: sp: Spell out: Used to indicate that an abbreviation should be spelled out, such as in its first use stet: Let it stand: Indicates that proofreading marks should be ignored and the copy unchanged tr: transpose: Transpose the two words selected wf: Wrong font: Put text in correct font ww [3 ...