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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.
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.
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.
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]
The Pronunciation Lexicon Specification (PLS) is a W3C Recommendation, which is designed to enable interoperable specification of pronunciation information for both speech recognition and speech synthesis engines within voice browsing applications. The language is intended to be easy to use by developers while supporting the accurate ...
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).