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Statistical language models


Language models are a major area of research and development, and crucial for the success of a speech recognition system . Chapter 7 should be consulted on this topic.

In speech recognition systems , the mapping of the digitised acoustic forms of words on to symbolic representations for use as lexical lookup keys is performed by stochastic speech recognisers, which may incorporate information about the phonological structure of a language to a greater or lesser extent, with word models  for matching with the acoustic analysis of the speech signal. Details of standard practice can be easily be found in the literature [Waibel & Lee (1990)].

In written language processing, a comparable task is Optical Character Recognition (OCR) , and in particular, handwriting recognition; there is no comparable task in conventional natural language processing  or computational linguistics, where letters are uniquely identified by digital codes, and dictionary   access may be trivially optimised by encoding letter sequences into tries  (letter trees, letter-based decision trees). However, in linguistic terms, in each case the task is the identification of word forms as lexical keys.

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