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The database is distributed as a plain text file with one entry to a line in the format "WORD <pronunciation>" with a two-space separator between the parts. If multiple pronunciations are available for a word, variants are identified using numbered versions (e.g. WORD(1)).
The language is intended to be easy to use by developers while supporting the accurate specification of pronunciation information for international use. The language allows one or more pronunciations for a word or phrase to be specified using a standard pronunciation alphabet or if necessary using vendor specific alphabets.
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.
Throughout Wikipedia, the pronunciation of words is indicated using the International Phonetic Alphabet (IPA). The following tables list the IPA symbols used for English words and pronunciations. Please note that several of these symbols are used in ways that are specific to Wikipedia, and differ from those used by dictionaries.
For example, the words me and pony have the same sound at the end, but use different letters. Teaching students to read words by blending: identifying the graphemes (letters) in the word, recalling the corresponding phonemes (sounds), and saying the phonemes together to form the sound of the whole word.
The earliest work on pronunciation assessment avoided measuring genuine listener intelligibility, [10] a shortcoming corrected in 2011 at the Toyohashi University of Technology, [11] and included in the Versant high-stakes English fluency assessment from Pearson [12] and mobile apps from 17zuoye Education & Technology, [13] but still missing in 2023 products from Google Search, [14] Microsoft ...
So readers looking up an unfamiliar word in a dictionary may find, on seeing the pronunciation respelling, that the word is in fact already known to them orally. By the same token, those who hear an unfamiliar spoken word may see several possible matches in a dictionary and must rely on the pronunciation respellings to find the correct match. [4]
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.