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It is commonly used to generate representations for speech recognition (ASR), e.g. the CMU Sphinx system, and speech synthesis (TTS), e.g. the Festival system. CMUdict can be used as a training corpus for building statistical grapheme-to-phoneme (g2p) models [1] that will generate pronunciations for words not yet included in the dictionary.
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.
For example, you may pronounce cot and caught, do and dew, or marry and merry the same. This often happens because of dialect variation (see our articles English phonology and International Phonetic Alphabet chart for English dialects). If this is the case, you will pronounce those symbols the same for other words as well. [1]
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.
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]